------------------------------------------------------------------------------------------------------------------
      name:  <unnamed>
       log:  /Users/Allan/Dropbox/!!Papers/Liberal Peace/12-02-21_ISQ_commentary/DOR_ISQ_2013_Replication/M_Rep/12
> -11-23_log.log
  log type:  text
 opened on:  23 Nov 2012, 08:27:36

. 
. set seed 74928348

. cd "$filetree"
/Users/Allan/Dropbox/!!Papers/Liberal Peace/12-02-21_ISQ_commentary/DOR_ISQ_2013_Replication/M_Rep

. 
. *The next section will also take some time. 
. do "12-06-19_mimputation.do"

. *12-06-19_mimputation.do
. 
. clear

. 
. cd "$filetree"
/Users/Allan/Dropbox/!!Papers/Liberal Peace/12-02-21_ISQ_commentary/DOR_ISQ_2013_Replication/M_Rep

. 
. set more off

. 
. use "12-05-28_dem_CIE_analysis.dta"

. 
. drop if year<1950
(0 observations deleted)

. drop if year>2001
(0 observations deleted)

. 
. *merge pruned beck and webb data
. 
. 
. rename ccode1 ccode

. sort ccode year

. merge m:1 ccode year using "country_year_mimpute.dta"
ccode was int now float
year was int now float

    Result                           # of obs.
    -----------------------------------------
    not matched                        69,697
        from master                    69,671  (_merge==1)
        from using                         26  (_merge==2)

    matched                           476,507  (_merge==3)
    -----------------------------------------

. 
. 
. keep if _merge==3
(69697 observations deleted)

. drop abbrev _merge majpow

. 
. foreach x of varlist mid fmid war region lngdppc lnlifedeer lncap lnmilper lnmilex lnirst lnenergy lnupop lntpop
>  lntottr lnlifepen lnacli {
  2. rename `x' `x'1
  3. }

. 
. 
. rename ccode ccode1

. rename ccode2 ccode

. sort ccode year

. 
. merge m:1 ccode year using "country_year_mimpute.dta"
ccode was int now float
(label rmideast already defined)

    Result                           # of obs.
    -----------------------------------------
    not matched                        40,018
        from master                    39,966  (_merge==1)
        from using                         52  (_merge==2)

    matched                           436,541  (_merge==3)
    -----------------------------------------

. 
. 
. keep if _merge==3
(40018 observations deleted)

. drop abbrev _merge majpow

. 
. rename ccode ccode2

. 
. foreach x of varlist mid fmid war region lngdppc lnlifedeer lncap lnmilper lnmilex lnirst lnenergy lnupop lntpop
>  lntottr lnlifepen lnacli {
  2. rename `x' `x'2
  3. }

. 
. gen lnlifedeerl=min(lnlifedeer1, lnlifedeer2) if lnlifedeer1~=. & lnlifedeer2~=.
(398484 missing values generated)

. gen lngdppcl=min(lngdppc1, lngdppc2) if lngdppc1~=. & lngdppc2~=.
(15640 missing values generated)

. gen lncapl=min(lncap1, lncap2) if lncap1~=. & lncap2~=.

. gen lnmilperl=min(lnmilper1, lnmilper2) if lnmilper1~=. & lnmilper2~=.
(28373 missing values generated)

. gen lnmilexl=min(lnmilex1, lnmilex2) if lnmilex1~=. & lnmilex2~=.
(44272 missing values generated)

. gen lnirstl=min(lnirst1, lnirst2) if lnirst1~=. & lnirst2~=.
(325783 missing values generated)

. gen lnenergyl=min(lnenergy1, lnenergy2) if lnenergy1~=. & lnenergy2~=.
(5567 missing values generated)

. gen lnupopl=min(lnupop1, lnupop2) if lnupop1~=. & lnupop2~=.
(60455 missing values generated)

. gen lntpopl=min(lntpop1, lntpop2) if lntpop1~=. & lntpop2~=.

. gen lntottrl=min(lntottr1, lntottr2) if lntottr1~=. & lntottr2~=.
(15640 missing values generated)

. gen lnlifepenl=min(lnlifepen1, lnlifepen2) if lnlifepen1~=. & lnlifepen2~=.
(398924 missing values generated)

. gen lnaclil=min(lnacli1, lnacli2) if lnacli1~=. & lnacli2~=.
(419714 missing values generated)

. 
. gen lnlifedeerh=max(lnlifedeer1, lnlifedeer2) if lnlifedeer1~=. & lnlifedeer2~=.
(398484 missing values generated)

. gen lngdppch=max(lngdppc1, lngdppc2) if lngdppc1~=. & lngdppc2~=.
(15640 missing values generated)

. gen lncaph=max(lncap1, lncap2) if lncap1~=. & lncap2~=.

. gen lnmilperh=max(lnmilper1, lnmilper2) if lnmilper1~=. & lnmilper2~=.
(28373 missing values generated)

. gen lnmilexh=max(lnmilex1, lnmilex2) if lnmilex1~=. & lnmilex2~=.
(44272 missing values generated)

. gen lnirsth=max(lnirst1, lnirst2) if lnirst1~=. & lnirst2~=.
(325783 missing values generated)

. gen lnenergyh=max(lnenergy1, lnenergy2) if lnenergy1~=. & lnenergy2~=.
(5567 missing values generated)

. gen lnupoph=max(lnupop1, lnupop2) if lnupop1~=. & lnupop2~=.
(60455 missing values generated)

. gen lntpoph=max(lntpop1, lntpop2) if lntpop1~=. & lntpop2~=.

. gen lntottrh=max(lntottr1, lntottr2) if lntottr1~=. & lntottr2~=.
(15640 missing values generated)

. gen lnlifepenh=max(lnlifepen1, lnlifepen2) if lnlifepen1~=. & lnlifepen2~=.
(398924 missing values generated)

. gen lnaclih=max(lnacli1, lnacli2) if lnacli1~=. & lnacli2~=.
(419714 missing values generated)

. 
. *simplify dataset:
. keep country_name1 country_name2 ccode1 ccode2 year abbrev1 abbrev2 cwmid cwongo cwfatald dyadid lncprt mjpw dml
>  dmh contigl lndist polity22 contig distance numstate numGPs cwongonm cwmidnm cwpceyrs midonsl midongl fmidonsl 
> fmidongl warl midyears midyears2 midyears3 fmidyears fmidyears2 fmidyears3 waryears waryears2 waryears3 tpop MMC
> IE lnlifedeerl lngdppcl lnlifepenl lnlifedeerh lngdppch lnlifepenh

. 
. count
436541

. 
. gen m=0

. 
. sort ccode1 ccode2 year

. 
. *For all years 1950-2001
. count if lnlifedeerl~=.
38057

. *38057
. local interpolated=r(N)

. *divide by 5 since technically Beck and Webb only coded every five years. 
. di `interpolated'/5
7611.4

. local original=`interpolated'/5

. *7611
. 
. count
436541

. local all=r(N)

. *436541
. 
. *Proportion of dyad-years originally coded:
. di `original'/`all'
.0174357

. *.0174357
. 
. *Proportion of dyad-years originally coded or interpolated:
. di `interpolated'/`all'
.08717852

. *.08717852
. 
. *Restricting to 1960-2000
. count if lnlifedeerl~=. & year>=1960 & year<2001
38057

. local interpolated=r(N)

. local original=`interpolated'/5

. 
. count if year>=1960 & year<2001
392741

. local all=r(N)

. 
. *Proportion of dyad-years originally coded:
. di `original'/`all'
.0193802

. *.0193802
. 
. *Proportion of dyad-years originally coded or interpolated:
. di `interpolated'/`all'
.09690101

. *.09690101
. 
. 
. *Need to drop:
. count if year<1960
31554

. local drop=r(N)

. count
436541

. local all=r(N)

. 
. di `drop'/`all'
.07228187

. 
. *gen _mi_id=_n
. 
. sort ccode1 ccode2 year

. save "mi_0.dta", replace
(note: file mi_0.dta not found)
file mi_0.dta saved

. 
. 
. 
. clear

. use "12-05-28_dem_CIE_analysis.dta"

. 
. drop if year<1950
(0 observations deleted)

. drop if year>2001
(0 observations deleted)

. 
. 
. *imputation 1
. rename ccode1 ccode

. sort ccode year

. merge m:1 ccode year using "outdatatotal1211171.dta"
ccode was int now double
year was int now double
numstate was int now long
numGPs was byte now long
polity2 was byte now long

    Result                           # of obs.
    -----------------------------------------
    not matched                        69,697
        from master                    69,671  (_merge==1)
        from using                         26  (_merge==2)

    matched                           476,507  (_merge==3)
    -----------------------------------------

. 
. tab country_name1 if _merge==1, sort

       COW CCode Num for state 1 |      Freq.     Percent        Cum.
---------------------------------+-----------------------------------
                      Luxembourg |      5,560        7.98        7.98
                        Barbados |      5,485        7.87       15.85
                         Bahamas |      4,776        6.86       22.71
                         Grenada |      4,418        6.34       29.05
                         Iceland |      4,173        5.99       35.04
                           Malta |      4,165        5.98       41.02
                        Dominica |      3,886        5.58       46.59
                        Suriname |      3,881        5.57       52.17
                     Saint Lucia |      3,696        5.30       57.47
Saint Vincent and the Grenadines |      3,673        5.27       62.74
             Antigua and Barbuda |      3,366        4.83       67.57
                          Belize |      3,305        4.74       72.32
           Saint Kitts and Nevis |      3,057        4.39       76.70
                      Cape Verde |      2,678        3.84       80.55
           Sao Tome and Principe |      2,651        3.81       84.35
                   Liechtenstein |      1,703        2.44       86.80
                      Seychelles |      1,423        2.04       88.84
                      San Marino |      1,327        1.90       90.75
                          Monaco |      1,315        1.89       92.63
                         Andorra |      1,279        1.84       94.47
          Bosnia and Herzegovina |        893        1.28       95.75
                        Maldives |        607        0.87       96.62
                         Lebanon |        578        0.83       97.45
      German Democratic Republic |        232        0.33       97.78
                        Zimbabwe |        230        0.33       98.11
                          Brunei |        203        0.29       98.41
                            Peru |        162        0.23       98.64
                        Cambodia |        119        0.17       98.81
                           Ghana |        113        0.16       98.97
                         Vanuatu |        104        0.15       99.12
                         Tunisia |        101        0.14       99.27
                        Zanzibar |         90        0.13       99.39
                          Kuwait |         79        0.11       99.51
                          Uganda |         70        0.10       99.61
                         Hungary |         51        0.07       99.68
                     Afghanistan |         42        0.06       99.74
                Marshall Islands |         30        0.04       99.78
                           Syria |         27        0.04       99.82
                        Kiribati |         23        0.03       99.86
                      Bangladesh |         16        0.02       99.88
                           Palau |         16        0.02       99.90
                           Tonga |         15        0.02       99.92
                          Tuvalu |         14        0.02       99.94
                           Nauru |         12        0.02       99.96
             Republic of Vietnam |         12        0.02       99.98
                      Micronesia |         11        0.02       99.99
                            Laos |          4        0.01      100.00
---------------------------------+-----------------------------------
                           Total |     69,671      100.00

. *small countries not matched
. 
. keep if _merge==3
(69697 observations deleted)

. drop abbrev _merge majpow

. 
. 
. foreach x of varlist mid fmid war region lngdppc lnlifedeer lncap lnmilper lnmilex lnirst lnenergy lnupop lntpop
>  lntottr lnlifepen lnacli {
  2. rename `x' `x'1
  3. }

. 
. rename ccode ccode1

. rename ccode2 ccode

. sort ccode year

. 
. merge m:1 ccode year using "outdatatotal1211171.dta"
ccode was int now double

    Result                           # of obs.
    -----------------------------------------
    not matched                        40,018
        from master                    39,966  (_merge==1)
        from using                         52  (_merge==2)

    matched                           436,541  (_merge==3)
    -----------------------------------------

. 
. keep if _merge==3
(40018 observations deleted)

. drop abbrev _merge majpow

. 
. rename ccode ccode2

. 
. foreach x of varlist mid fmid war region lngdppc lnlifedeer lncap lnmilper lnmilex lnirst lnenergy lnupop lntpop
>  lntottr lnlifepen lnacli {
  2. rename `x' `x'2
  3. }

. 
. gen lnlifedeerl=min(lnlifedeer1, lnlifedeer2) if lnlifedeer1~=. & lnlifedeer2~=.

. gen lngdppcl=min(lngdppc1, lngdppc2) if lngdppc1~=. & lngdppc2~=.

. gen lncapl=min(lncap1, lncap2) if lncap1~=. & lncap2~=.

. gen lnmilperl=min(lnmilper1, lnmilper2) if lnmilper1~=. & lnmilper2~=.

. gen lnmilexl=min(lnmilex1, lnmilex2) if lnmilex1~=. & lnmilex2~=.

. gen lnirstl=min(lnirst1, lnirst2) if lnirst1~=. & lnirst2~=.

. gen lnenergyl=min(lnenergy1, lnenergy2) if lnenergy1~=. & lnenergy2~=.

. gen lnupopl=min(lnupop1, lnupop2) if lnupop1~=. & lnupop2~=.

. gen lntpopl=min(lntpop1, lntpop2) if lntpop1~=. & lntpop2~=.

. gen lntottrl=min(lntottr1, lntottr2) if lntottr1~=. & lntottr2~=.

. gen lnlifepenl=min(lnlifepen1, lnlifepen2) if lnlifepen1~=. & lnlifepen2~=.

. gen lnaclil=min(lnacli1, lnacli2) if lnacli1~=. & lnacli2~=.

. 
. gen lnlifedeerh=max(lnlifedeer1, lnlifedeer2) if lnlifedeer1~=. & lnlifedeer2~=.

. gen lngdppch=max(lngdppc1, lngdppc2) if lngdppc1~=. & lngdppc2~=.

. gen lncaph=max(lncap1, lncap2) if lncap1~=. & lncap2~=.

. gen lnmilperh=max(lnmilper1, lnmilper2) if lnmilper1~=. & lnmilper2~=.

. gen lnmilexh=max(lnmilex1, lnmilex2) if lnmilex1~=. & lnmilex2~=.

. gen lnirsth=max(lnirst1, lnirst2) if lnirst1~=. & lnirst2~=.

. gen lnenergyh=max(lnenergy1, lnenergy2) if lnenergy1~=. & lnenergy2~=.

. gen lnupoph=max(lnupop1, lnupop2) if lnupop1~=. & lnupop2~=.

. gen lntpoph=max(lntpop1, lntpop2) if lntpop1~=. & lntpop2~=.

. gen lntottrh=max(lntottr1, lntottr2) if lntottr1~=. & lntottr2~=.

. gen lnlifepenh=max(lnlifepen1, lnlifepen2) if lnlifepen1~=. & lnlifepen2~=.

. gen lnaclih=max(lnacli1, lnacli2) if lnacli1~=. & lnacli2~=.

. 
. 
. *simplify dataset:
. keep country_name1 country_name2 ccode1 ccode2 year abbrev1 abbrev2 cwmid cwongo cwfatald dyadid lncprt mjpw dml
>  dmh contigl lndist polity22 contig distance numstate numGPs cwongonm cwmidnm cwpceyrs midonsl midongl fmidonsl 
> fmidongl warl midyears midyears2 midyears3 fmidyears fmidyears2 fmidyears3 waryears waryears2 waryears3 tpop MMC
> IE lnlifedeerl lngdppcl lnlifepenl lnlifedeerh lngdppch lnlifepenh

. 
. 
. count
436541

. 
. gen m=1

. sort ccode1 ccode2 year

. save "mi_1.dta", replace
(note: file mi_1.dta not found)
file mi_1.dta saved

. 
. 
. ***
. 
. 
. 
. *imputation 2
. clear

. use "12-05-28_dem_CIE_analysis.dta"

. 
. drop if year<1950
(0 observations deleted)

. drop if year>2001
(0 observations deleted)

. 
. rename ccode1 ccode

. sort ccode year

. merge m:1 ccode year using "outdatatotal1211172.dta"
ccode was int now double
year was int now double
numstate was int now long
numGPs was byte now long
polity2 was byte now long

    Result                           # of obs.
    -----------------------------------------
    not matched                        69,697
        from master                    69,671  (_merge==1)
        from using                         26  (_merge==2)

    matched                           476,507  (_merge==3)
    -----------------------------------------

. 
. tab country_name1 if _merge==1, sort

       COW CCode Num for state 1 |      Freq.     Percent        Cum.
---------------------------------+-----------------------------------
                      Luxembourg |      5,560        7.98        7.98
                        Barbados |      5,485        7.87       15.85
                         Bahamas |      4,776        6.86       22.71
                         Grenada |      4,418        6.34       29.05
                         Iceland |      4,173        5.99       35.04
                           Malta |      4,165        5.98       41.02
                        Dominica |      3,886        5.58       46.59
                        Suriname |      3,881        5.57       52.17
                     Saint Lucia |      3,696        5.30       57.47
Saint Vincent and the Grenadines |      3,673        5.27       62.74
             Antigua and Barbuda |      3,366        4.83       67.57
                          Belize |      3,305        4.74       72.32
           Saint Kitts and Nevis |      3,057        4.39       76.70
                      Cape Verde |      2,678        3.84       80.55
           Sao Tome and Principe |      2,651        3.81       84.35
                   Liechtenstein |      1,703        2.44       86.80
                      Seychelles |      1,423        2.04       88.84
                      San Marino |      1,327        1.90       90.75
                          Monaco |      1,315        1.89       92.63
                         Andorra |      1,279        1.84       94.47
          Bosnia and Herzegovina |        893        1.28       95.75
                        Maldives |        607        0.87       96.62
                         Lebanon |        578        0.83       97.45
      German Democratic Republic |        232        0.33       97.78
                        Zimbabwe |        230        0.33       98.11
                          Brunei |        203        0.29       98.41
                            Peru |        162        0.23       98.64
                        Cambodia |        119        0.17       98.81
                           Ghana |        113        0.16       98.97
                         Vanuatu |        104        0.15       99.12
                         Tunisia |        101        0.14       99.27
                        Zanzibar |         90        0.13       99.39
                          Kuwait |         79        0.11       99.51
                          Uganda |         70        0.10       99.61
                         Hungary |         51        0.07       99.68
                     Afghanistan |         42        0.06       99.74
                Marshall Islands |         30        0.04       99.78
                           Syria |         27        0.04       99.82
                        Kiribati |         23        0.03       99.86
                      Bangladesh |         16        0.02       99.88
                           Palau |         16        0.02       99.90
                           Tonga |         15        0.02       99.92
                          Tuvalu |         14        0.02       99.94
                           Nauru |         12        0.02       99.96
             Republic of Vietnam |         12        0.02       99.98
                      Micronesia |         11        0.02       99.99
                            Laos |          4        0.01      100.00
---------------------------------+-----------------------------------
                           Total |     69,671      100.00

. *small countries not matched
. 
. keep if _merge==3
(69697 observations deleted)

. drop abbrev _merge majpow

. 
. 
. foreach x of varlist mid fmid war region lngdppc lnlifedeer lncap lnmilper lnmilex lnirst lnenergy lnupop lntpop
>  lntottr lnlifepen lnacli {
  2. rename `x' `x'1
  3. }

. 
. rename ccode ccode1

. rename ccode2 ccode

. sort ccode year

. 
. merge m:1 ccode year using "outdatatotal1211172.dta"
ccode was int now double

    Result                           # of obs.
    -----------------------------------------
    not matched                        40,018
        from master                    39,966  (_merge==1)
        from using                         52  (_merge==2)

    matched                           436,541  (_merge==3)
    -----------------------------------------

. 
. keep if _merge==3
(40018 observations deleted)

. drop abbrev _merge majpow

. 
. rename ccode ccode2

. 
. foreach x of varlist mid fmid war region lngdppc lnlifedeer lncap lnmilper lnmilex lnirst lnenergy lnupop lntpop
>  lntottr lnlifepen lnacli {
  2. rename `x' `x'2
  3. }

. 
. gen lnlifedeerl=min(lnlifedeer1, lnlifedeer2) if lnlifedeer1~=. & lnlifedeer2~=.

. gen lngdppcl=min(lngdppc1, lngdppc2) if lngdppc1~=. & lngdppc2~=.

. gen lncapl=min(lncap1, lncap2) if lncap1~=. & lncap2~=.

. gen lnmilperl=min(lnmilper1, lnmilper2) if lnmilper1~=. & lnmilper2~=.

. gen lnmilexl=min(lnmilex1, lnmilex2) if lnmilex1~=. & lnmilex2~=.

. gen lnirstl=min(lnirst1, lnirst2) if lnirst1~=. & lnirst2~=.

. gen lnenergyl=min(lnenergy1, lnenergy2) if lnenergy1~=. & lnenergy2~=.

. gen lnupopl=min(lnupop1, lnupop2) if lnupop1~=. & lnupop2~=.

. gen lntpopl=min(lntpop1, lntpop2) if lntpop1~=. & lntpop2~=.

. gen lntottrl=min(lntottr1, lntottr2) if lntottr1~=. & lntottr2~=.

. gen lnlifepenl=min(lnlifepen1, lnlifepen2) if lnlifepen1~=. & lnlifepen2~=.

. gen lnaclil=min(lnacli1, lnacli2) if lnacli1~=. & lnacli2~=.

. 
. gen lnlifedeerh=max(lnlifedeer1, lnlifedeer2) if lnlifedeer1~=. & lnlifedeer2~=.

. gen lngdppch=max(lngdppc1, lngdppc2) if lngdppc1~=. & lngdppc2~=.

. gen lncaph=max(lncap1, lncap2) if lncap1~=. & lncap2~=.

. gen lnmilperh=max(lnmilper1, lnmilper2) if lnmilper1~=. & lnmilper2~=.

. gen lnmilexh=max(lnmilex1, lnmilex2) if lnmilex1~=. & lnmilex2~=.

. gen lnirsth=max(lnirst1, lnirst2) if lnirst1~=. & lnirst2~=.

. gen lnenergyh=max(lnenergy1, lnenergy2) if lnenergy1~=. & lnenergy2~=.

. gen lnupoph=max(lnupop1, lnupop2) if lnupop1~=. & lnupop2~=.

. gen lntpoph=max(lntpop1, lntpop2) if lntpop1~=. & lntpop2~=.

. gen lntottrh=max(lntottr1, lntottr2) if lntottr1~=. & lntottr2~=.

. gen lnlifepenh=max(lnlifepen1, lnlifepen2) if lnlifepen1~=. & lnlifepen2~=.

. gen lnaclih=max(lnacli1, lnacli2) if lnacli1~=. & lnacli2~=.

. 
. *simplify dataset:
. keep country_name1 country_name2 ccode1 ccode2 year abbrev1 abbrev2 cwmid cwongo cwfatald dyadid lncprt mjpw dml
>  dmh contigl lndist polity22 contig distance numstate numGPs cwongonm cwmidnm cwpceyrs midonsl midongl fmidonsl 
> fmidongl warl midyears midyears2 midyears3 fmidyears fmidyears2 fmidyears3 waryears waryears2 waryears3 tpop MMC
> IE lnlifedeerl lngdppcl lnlifepenl lnlifedeerh lngdppch lnlifepenh

. 
. count
436541

. 
. gen m=2

. 
. sort ccode1 ccode2 year

. save "mi_2.dta", replace
(note: file mi_2.dta not found)
file mi_2.dta saved

. 
. 
. *imputation 3
. clear

. use "12-05-28_dem_CIE_analysis.dta"

. 
. drop if year<1950
(0 observations deleted)

. drop if year>2001
(0 observations deleted)

. 
. rename ccode1 ccode

. sort ccode year

. merge m:1 ccode year using "outdatatotal1211173.dta"
ccode was int now double
year was int now double
numstate was int now long
numGPs was byte now long
polity2 was byte now long

    Result                           # of obs.
    -----------------------------------------
    not matched                        69,697
        from master                    69,671  (_merge==1)
        from using                         26  (_merge==2)

    matched                           476,507  (_merge==3)
    -----------------------------------------

. 
. tab country_name1 if _merge==1, sort

       COW CCode Num for state 1 |      Freq.     Percent        Cum.
---------------------------------+-----------------------------------
                      Luxembourg |      5,560        7.98        7.98
                        Barbados |      5,485        7.87       15.85
                         Bahamas |      4,776        6.86       22.71
                         Grenada |      4,418        6.34       29.05
                         Iceland |      4,173        5.99       35.04
                           Malta |      4,165        5.98       41.02
                        Dominica |      3,886        5.58       46.59
                        Suriname |      3,881        5.57       52.17
                     Saint Lucia |      3,696        5.30       57.47
Saint Vincent and the Grenadines |      3,673        5.27       62.74
             Antigua and Barbuda |      3,366        4.83       67.57
                          Belize |      3,305        4.74       72.32
           Saint Kitts and Nevis |      3,057        4.39       76.70
                      Cape Verde |      2,678        3.84       80.55
           Sao Tome and Principe |      2,651        3.81       84.35
                   Liechtenstein |      1,703        2.44       86.80
                      Seychelles |      1,423        2.04       88.84
                      San Marino |      1,327        1.90       90.75
                          Monaco |      1,315        1.89       92.63
                         Andorra |      1,279        1.84       94.47
          Bosnia and Herzegovina |        893        1.28       95.75
                        Maldives |        607        0.87       96.62
                         Lebanon |        578        0.83       97.45
      German Democratic Republic |        232        0.33       97.78
                        Zimbabwe |        230        0.33       98.11
                          Brunei |        203        0.29       98.41
                            Peru |        162        0.23       98.64
                        Cambodia |        119        0.17       98.81
                           Ghana |        113        0.16       98.97
                         Vanuatu |        104        0.15       99.12
                         Tunisia |        101        0.14       99.27
                        Zanzibar |         90        0.13       99.39
                          Kuwait |         79        0.11       99.51
                          Uganda |         70        0.10       99.61
                         Hungary |         51        0.07       99.68
                     Afghanistan |         42        0.06       99.74
                Marshall Islands |         30        0.04       99.78
                           Syria |         27        0.04       99.82
                        Kiribati |         23        0.03       99.86
                      Bangladesh |         16        0.02       99.88
                           Palau |         16        0.02       99.90
                           Tonga |         15        0.02       99.92
                          Tuvalu |         14        0.02       99.94
                           Nauru |         12        0.02       99.96
             Republic of Vietnam |         12        0.02       99.98
                      Micronesia |         11        0.02       99.99
                            Laos |          4        0.01      100.00
---------------------------------+-----------------------------------
                           Total |     69,671      100.00

. *small countries not matched
. 
. keep if _merge==3
(69697 observations deleted)

. drop abbrev _merge majpow

. 
. 
. foreach x of varlist mid fmid war region lngdppc lnlifedeer lncap lnmilper lnmilex lnirst lnenergy lnupop lntpop
>  lntottr lnlifepen lnacli {
  2. rename `x' `x'1
  3. }

. 
. rename ccode ccode1

. rename ccode2 ccode

. sort ccode year

. 
. merge m:1 ccode year using "outdatatotal1211173.dta"
ccode was int now double

    Result                           # of obs.
    -----------------------------------------
    not matched                        40,018
        from master                    39,966  (_merge==1)
        from using                         52  (_merge==2)

    matched                           436,541  (_merge==3)
    -----------------------------------------

. 
. keep if _merge==3
(40018 observations deleted)

. drop abbrev _merge majpow

. 
. rename ccode ccode2

. 
. foreach x of varlist mid fmid war region lngdppc lnlifedeer lncap lnmilper lnmilex lnirst lnenergy lnupop lntpop
>  lntottr lnlifepen lnacli {
  2. rename `x' `x'2
  3. }

. 
. gen lnlifedeerl=min(lnlifedeer1, lnlifedeer2) if lnlifedeer1~=. & lnlifedeer2~=.

. gen lngdppcl=min(lngdppc1, lngdppc2) if lngdppc1~=. & lngdppc2~=.

. gen lncapl=min(lncap1, lncap2) if lncap1~=. & lncap2~=.

. gen lnmilperl=min(lnmilper1, lnmilper2) if lnmilper1~=. & lnmilper2~=.

. gen lnmilexl=min(lnmilex1, lnmilex2) if lnmilex1~=. & lnmilex2~=.

. gen lnirstl=min(lnirst1, lnirst2) if lnirst1~=. & lnirst2~=.

. gen lnenergyl=min(lnenergy1, lnenergy2) if lnenergy1~=. & lnenergy2~=.

. gen lnupopl=min(lnupop1, lnupop2) if lnupop1~=. & lnupop2~=.

. gen lntpopl=min(lntpop1, lntpop2) if lntpop1~=. & lntpop2~=.

. gen lntottrl=min(lntottr1, lntottr2) if lntottr1~=. & lntottr2~=.

. gen lnlifepenl=min(lnlifepen1, lnlifepen2) if lnlifepen1~=. & lnlifepen2~=.

. gen lnaclil=min(lnacli1, lnacli2) if lnacli1~=. & lnacli2~=.

. 
. gen lnlifedeerh=max(lnlifedeer1, lnlifedeer2) if lnlifedeer1~=. & lnlifedeer2~=.

. gen lngdppch=max(lngdppc1, lngdppc2) if lngdppc1~=. & lngdppc2~=.

. gen lncaph=max(lncap1, lncap2) if lncap1~=. & lncap2~=.

. gen lnmilperh=max(lnmilper1, lnmilper2) if lnmilper1~=. & lnmilper2~=.

. gen lnmilexh=max(lnmilex1, lnmilex2) if lnmilex1~=. & lnmilex2~=.

. gen lnirsth=max(lnirst1, lnirst2) if lnirst1~=. & lnirst2~=.

. gen lnenergyh=max(lnenergy1, lnenergy2) if lnenergy1~=. & lnenergy2~=.

. gen lnupoph=max(lnupop1, lnupop2) if lnupop1~=. & lnupop2~=.

. gen lntpoph=max(lntpop1, lntpop2) if lntpop1~=. & lntpop2~=.

. gen lntottrh=max(lntottr1, lntottr2) if lntottr1~=. & lntottr2~=.

. gen lnlifepenh=max(lnlifepen1, lnlifepen2) if lnlifepen1~=. & lnlifepen2~=.

. gen lnaclih=max(lnacli1, lnacli2) if lnacli1~=. & lnacli2~=.

. 
. *simplify dataset:
. keep country_name1 country_name2 ccode1 ccode2 year abbrev1 abbrev2 cwmid cwongo cwfatald dyadid lncprt mjpw dml
>  dmh contigl lndist polity22 contig distance numstate numGPs cwongonm cwmidnm cwpceyrs midonsl midongl fmidonsl 
> fmidongl warl midyears midyears2 midyears3 fmidyears fmidyears2 fmidyears3 waryears waryears2 waryears3 tpop MMC
> IE lnlifedeerl lngdppcl lnlifepenl lnlifedeerh lngdppch lnlifepenh

. 
. count
436541

. 
. gen m=3

. 
. sort ccode1 ccode2 year

. save "mi_3.dta", replace
(note: file mi_3.dta not found)
file mi_3.dta saved

. 
. 
. 
. 
. 
. 
. *imputation 4
. clear

. use "12-05-28_dem_CIE_analysis.dta"

. 
. drop if year<1950
(0 observations deleted)

. drop if year>2001
(0 observations deleted)

. 
. rename ccode1 ccode

. sort ccode year

. merge m:1 ccode year using "outdatatotal1211174.dta"
ccode was int now double
year was int now double
numstate was int now long
numGPs was byte now long
polity2 was byte now long

    Result                           # of obs.
    -----------------------------------------
    not matched                        69,697
        from master                    69,671  (_merge==1)
        from using                         26  (_merge==2)

    matched                           476,507  (_merge==3)
    -----------------------------------------

. 
. tab country_name1 if _merge==1, sort

       COW CCode Num for state 1 |      Freq.     Percent        Cum.
---------------------------------+-----------------------------------
                      Luxembourg |      5,560        7.98        7.98
                        Barbados |      5,485        7.87       15.85
                         Bahamas |      4,776        6.86       22.71
                         Grenada |      4,418        6.34       29.05
                         Iceland |      4,173        5.99       35.04
                           Malta |      4,165        5.98       41.02
                        Dominica |      3,886        5.58       46.59
                        Suriname |      3,881        5.57       52.17
                     Saint Lucia |      3,696        5.30       57.47
Saint Vincent and the Grenadines |      3,673        5.27       62.74
             Antigua and Barbuda |      3,366        4.83       67.57
                          Belize |      3,305        4.74       72.32
           Saint Kitts and Nevis |      3,057        4.39       76.70
                      Cape Verde |      2,678        3.84       80.55
           Sao Tome and Principe |      2,651        3.81       84.35
                   Liechtenstein |      1,703        2.44       86.80
                      Seychelles |      1,423        2.04       88.84
                      San Marino |      1,327        1.90       90.75
                          Monaco |      1,315        1.89       92.63
                         Andorra |      1,279        1.84       94.47
          Bosnia and Herzegovina |        893        1.28       95.75
                        Maldives |        607        0.87       96.62
                         Lebanon |        578        0.83       97.45
      German Democratic Republic |        232        0.33       97.78
                        Zimbabwe |        230        0.33       98.11
                          Brunei |        203        0.29       98.41
                            Peru |        162        0.23       98.64
                        Cambodia |        119        0.17       98.81
                           Ghana |        113        0.16       98.97
                         Vanuatu |        104        0.15       99.12
                         Tunisia |        101        0.14       99.27
                        Zanzibar |         90        0.13       99.39
                          Kuwait |         79        0.11       99.51
                          Uganda |         70        0.10       99.61
                         Hungary |         51        0.07       99.68
                     Afghanistan |         42        0.06       99.74
                Marshall Islands |         30        0.04       99.78
                           Syria |         27        0.04       99.82
                        Kiribati |         23        0.03       99.86
                      Bangladesh |         16        0.02       99.88
                           Palau |         16        0.02       99.90
                           Tonga |         15        0.02       99.92
                          Tuvalu |         14        0.02       99.94
                           Nauru |         12        0.02       99.96
             Republic of Vietnam |         12        0.02       99.98
                      Micronesia |         11        0.02       99.99
                            Laos |          4        0.01      100.00
---------------------------------+-----------------------------------
                           Total |     69,671      100.00

. *small countries not matched
. 
. keep if _merge==3
(69697 observations deleted)

. drop abbrev _merge majpow

. 
. 
. foreach x of varlist mid fmid war region lngdppc lnlifedeer lncap lnmilper lnmilex lnirst lnenergy lnupop lntpop
>  lntottr lnlifepen lnacli {
  2. rename `x' `x'1
  3. }

. 
. rename ccode ccode1

. rename ccode2 ccode

. sort ccode year

. 
. merge m:1 ccode year using "outdatatotal1211174.dta"
ccode was int now double

    Result                           # of obs.
    -----------------------------------------
    not matched                        40,018
        from master                    39,966  (_merge==1)
        from using                         52  (_merge==2)

    matched                           436,541  (_merge==3)
    -----------------------------------------

. 
. keep if _merge==3
(40018 observations deleted)

. drop abbrev _merge majpow

. 
. rename ccode ccode2

. 
. foreach x of varlist mid fmid war region lngdppc lnlifedeer lncap lnmilper lnmilex lnirst lnenergy lnupop lntpop
>  lntottr lnlifepen lnacli {
  2. rename `x' `x'2
  3. }

. 
. gen lnlifedeerl=min(lnlifedeer1, lnlifedeer2) if lnlifedeer1~=. & lnlifedeer2~=.

. gen lngdppcl=min(lngdppc1, lngdppc2) if lngdppc1~=. & lngdppc2~=.

. gen lncapl=min(lncap1, lncap2) if lncap1~=. & lncap2~=.

. gen lnmilperl=min(lnmilper1, lnmilper2) if lnmilper1~=. & lnmilper2~=.

. gen lnmilexl=min(lnmilex1, lnmilex2) if lnmilex1~=. & lnmilex2~=.

. gen lnirstl=min(lnirst1, lnirst2) if lnirst1~=. & lnirst2~=.

. gen lnenergyl=min(lnenergy1, lnenergy2) if lnenergy1~=. & lnenergy2~=.

. gen lnupopl=min(lnupop1, lnupop2) if lnupop1~=. & lnupop2~=.

. gen lntpopl=min(lntpop1, lntpop2) if lntpop1~=. & lntpop2~=.

. gen lntottrl=min(lntottr1, lntottr2) if lntottr1~=. & lntottr2~=.

. gen lnlifepenl=min(lnlifepen1, lnlifepen2) if lnlifepen1~=. & lnlifepen2~=.

. gen lnaclil=min(lnacli1, lnacli2) if lnacli1~=. & lnacli2~=.

. 
. gen lnlifedeerh=max(lnlifedeer1, lnlifedeer2) if lnlifedeer1~=. & lnlifedeer2~=.

. gen lngdppch=max(lngdppc1, lngdppc2) if lngdppc1~=. & lngdppc2~=.

. gen lncaph=max(lncap1, lncap2) if lncap1~=. & lncap2~=.

. gen lnmilperh=max(lnmilper1, lnmilper2) if lnmilper1~=. & lnmilper2~=.

. gen lnmilexh=max(lnmilex1, lnmilex2) if lnmilex1~=. & lnmilex2~=.

. gen lnirsth=max(lnirst1, lnirst2) if lnirst1~=. & lnirst2~=.

. gen lnenergyh=max(lnenergy1, lnenergy2) if lnenergy1~=. & lnenergy2~=.

. gen lnupoph=max(lnupop1, lnupop2) if lnupop1~=. & lnupop2~=.

. gen lntpoph=max(lntpop1, lntpop2) if lntpop1~=. & lntpop2~=.

. gen lntottrh=max(lntottr1, lntottr2) if lntottr1~=. & lntottr2~=.

. gen lnlifepenh=max(lnlifepen1, lnlifepen2) if lnlifepen1~=. & lnlifepen2~=.

. gen lnaclih=max(lnacli1, lnacli2) if lnacli1~=. & lnacli2~=.

. 
. *simplify dataset:
. keep country_name1 country_name2 ccode1 ccode2 year abbrev1 abbrev2 cwmid cwongo cwfatald dyadid lncprt mjpw dml
>  dmh contigl lndist polity22 contig distance numstate numGPs cwongonm cwmidnm cwpceyrs midonsl midongl fmidonsl 
> fmidongl warl midyears midyears2 midyears3 fmidyears fmidyears2 fmidyears3 waryears waryears2 waryears3 tpop MMC
> IE lnlifedeerl lngdppcl lnlifepenl lnlifedeerh lngdppch lnlifepenh

. 
. count
436541

. 
. gen m=4

. 
. sort ccode1 ccode2 year

. save "mi_4.dta", replace
(note: file mi_4.dta not found)
file mi_4.dta saved

. 
. 
. 
. *imputation 5
. clear

. use "12-05-28_dem_CIE_analysis.dta"

. 
. drop if year<1950
(0 observations deleted)

. drop if year>2001
(0 observations deleted)

. 
. rename ccode1 ccode

. sort ccode year

. merge m:1 ccode year using "outdatatotal1211175.dta"
ccode was int now double
year was int now double
numstate was int now long
numGPs was byte now long
polity2 was byte now long

    Result                           # of obs.
    -----------------------------------------
    not matched                        69,697
        from master                    69,671  (_merge==1)
        from using                         26  (_merge==2)

    matched                           476,507  (_merge==3)
    -----------------------------------------

. 
. tab country_name1 if _merge==1, sort

       COW CCode Num for state 1 |      Freq.     Percent        Cum.
---------------------------------+-----------------------------------
                      Luxembourg |      5,560        7.98        7.98
                        Barbados |      5,485        7.87       15.85
                         Bahamas |      4,776        6.86       22.71
                         Grenada |      4,418        6.34       29.05
                         Iceland |      4,173        5.99       35.04
                           Malta |      4,165        5.98       41.02
                        Dominica |      3,886        5.58       46.59
                        Suriname |      3,881        5.57       52.17
                     Saint Lucia |      3,696        5.30       57.47
Saint Vincent and the Grenadines |      3,673        5.27       62.74
             Antigua and Barbuda |      3,366        4.83       67.57
                          Belize |      3,305        4.74       72.32
           Saint Kitts and Nevis |      3,057        4.39       76.70
                      Cape Verde |      2,678        3.84       80.55
           Sao Tome and Principe |      2,651        3.81       84.35
                   Liechtenstein |      1,703        2.44       86.80
                      Seychelles |      1,423        2.04       88.84
                      San Marino |      1,327        1.90       90.75
                          Monaco |      1,315        1.89       92.63
                         Andorra |      1,279        1.84       94.47
          Bosnia and Herzegovina |        893        1.28       95.75
                        Maldives |        607        0.87       96.62
                         Lebanon |        578        0.83       97.45
      German Democratic Republic |        232        0.33       97.78
                        Zimbabwe |        230        0.33       98.11
                          Brunei |        203        0.29       98.41
                            Peru |        162        0.23       98.64
                        Cambodia |        119        0.17       98.81
                           Ghana |        113        0.16       98.97
                         Vanuatu |        104        0.15       99.12
                         Tunisia |        101        0.14       99.27
                        Zanzibar |         90        0.13       99.39
                          Kuwait |         79        0.11       99.51
                          Uganda |         70        0.10       99.61
                         Hungary |         51        0.07       99.68
                     Afghanistan |         42        0.06       99.74
                Marshall Islands |         30        0.04       99.78
                           Syria |         27        0.04       99.82
                        Kiribati |         23        0.03       99.86
                      Bangladesh |         16        0.02       99.88
                           Palau |         16        0.02       99.90
                           Tonga |         15        0.02       99.92
                          Tuvalu |         14        0.02       99.94
                           Nauru |         12        0.02       99.96
             Republic of Vietnam |         12        0.02       99.98
                      Micronesia |         11        0.02       99.99
                            Laos |          4        0.01      100.00
---------------------------------+-----------------------------------
                           Total |     69,671      100.00

. *small countries not matched
. 
. keep if _merge==3
(69697 observations deleted)

. drop abbrev _merge majpow

. 
. 
. foreach x of varlist mid fmid war region lngdppc lnlifedeer lncap lnmilper lnmilex lnirst lnenergy lnupop lntpop
>  lntottr lnlifepen lnacli {
  2. rename `x' `x'1
  3. }

. 
. rename ccode ccode1

. rename ccode2 ccode

. sort ccode year

. 
. merge m:1 ccode year using "outdatatotal1211175.dta"
ccode was int now double

    Result                           # of obs.
    -----------------------------------------
    not matched                        40,018
        from master                    39,966  (_merge==1)
        from using                         52  (_merge==2)

    matched                           436,541  (_merge==3)
    -----------------------------------------

. 
. keep if _merge==3
(40018 observations deleted)

. drop abbrev _merge majpow

. 
. rename ccode ccode2

. 
. foreach x of varlist mid fmid war region lngdppc lnlifedeer lncap lnmilper lnmilex lnirst lnenergy lnupop lntpop
>  lntottr lnlifepen lnacli {
  2. rename `x' `x'2
  3. }

. 
. gen lnlifedeerl=min(lnlifedeer1, lnlifedeer2) if lnlifedeer1~=. & lnlifedeer2~=.

. gen lngdppcl=min(lngdppc1, lngdppc2) if lngdppc1~=. & lngdppc2~=.

. gen lncapl=min(lncap1, lncap2) if lncap1~=. & lncap2~=.

. gen lnmilperl=min(lnmilper1, lnmilper2) if lnmilper1~=. & lnmilper2~=.

. gen lnmilexl=min(lnmilex1, lnmilex2) if lnmilex1~=. & lnmilex2~=.

. gen lnirstl=min(lnirst1, lnirst2) if lnirst1~=. & lnirst2~=.

. gen lnenergyl=min(lnenergy1, lnenergy2) if lnenergy1~=. & lnenergy2~=.

. gen lnupopl=min(lnupop1, lnupop2) if lnupop1~=. & lnupop2~=.

. gen lntpopl=min(lntpop1, lntpop2) if lntpop1~=. & lntpop2~=.

. gen lntottrl=min(lntottr1, lntottr2) if lntottr1~=. & lntottr2~=.

. gen lnlifepenl=min(lnlifepen1, lnlifepen2) if lnlifepen1~=. & lnlifepen2~=.

. gen lnaclil=min(lnacli1, lnacli2) if lnacli1~=. & lnacli2~=.

. 
. gen lnlifedeerh=max(lnlifedeer1, lnlifedeer2) if lnlifedeer1~=. & lnlifedeer2~=.

. gen lngdppch=max(lngdppc1, lngdppc2) if lngdppc1~=. & lngdppc2~=.

. gen lncaph=max(lncap1, lncap2) if lncap1~=. & lncap2~=.

. gen lnmilperh=max(lnmilper1, lnmilper2) if lnmilper1~=. & lnmilper2~=.

. gen lnmilexh=max(lnmilex1, lnmilex2) if lnmilex1~=. & lnmilex2~=.

. gen lnirsth=max(lnirst1, lnirst2) if lnirst1~=. & lnirst2~=.

. gen lnenergyh=max(lnenergy1, lnenergy2) if lnenergy1~=. & lnenergy2~=.

. gen lnupoph=max(lnupop1, lnupop2) if lnupop1~=. & lnupop2~=.

. gen lntpoph=max(lntpop1, lntpop2) if lntpop1~=. & lntpop2~=.

. gen lntottrh=max(lntottr1, lntottr2) if lntottr1~=. & lntottr2~=.

. gen lnlifepenh=max(lnlifepen1, lnlifepen2) if lnlifepen1~=. & lnlifepen2~=.

. gen lnaclih=max(lnacli1, lnacli2) if lnacli1~=. & lnacli2~=.

. 
. *simplify dataset:
. keep country_name1 country_name2 ccode1 ccode2 year abbrev1 abbrev2 cwmid cwongo cwfatald dyadid lncprt mjpw dml
>  dmh contigl lndist polity22 contig distance numstate numGPs cwongonm cwmidnm cwpceyrs midonsl midongl fmidonsl 
> fmidongl warl midyears midyears2 midyears3 fmidyears fmidyears2 fmidyears3 waryears waryears2 waryears3 tpop MMC
> IE lnlifedeerl lngdppcl lnlifepenl lnlifedeerh lngdppch lnlifepenh

. 
. count
436541

. 
. gen m=5

. 
. sort ccode1 ccode2 year

. save "mi_5.dta", replace
(note: file mi_5.dta not found)
file mi_5.dta saved

. 
. 
. 
. 
. *imputation 6
. clear

. use "12-05-28_dem_CIE_analysis.dta"

. 
. drop if year<1950
(0 observations deleted)

. drop if year>2001
(0 observations deleted)

. 
. rename ccode1 ccode

. sort ccode year

. merge m:1 ccode year using "outdatatotal1211176.dta"
ccode was int now double
year was int now double
numstate was int now long
numGPs was byte now long
polity2 was byte now long

    Result                           # of obs.
    -----------------------------------------
    not matched                        69,697
        from master                    69,671  (_merge==1)
        from using                         26  (_merge==2)

    matched                           476,507  (_merge==3)
    -----------------------------------------

. 
. tab country_name1 if _merge==1, sort

       COW CCode Num for state 1 |      Freq.     Percent        Cum.
---------------------------------+-----------------------------------
                      Luxembourg |      5,560        7.98        7.98
                        Barbados |      5,485        7.87       15.85
                         Bahamas |      4,776        6.86       22.71
                         Grenada |      4,418        6.34       29.05
                         Iceland |      4,173        5.99       35.04
                           Malta |      4,165        5.98       41.02
                        Dominica |      3,886        5.58       46.59
                        Suriname |      3,881        5.57       52.17
                     Saint Lucia |      3,696        5.30       57.47
Saint Vincent and the Grenadines |      3,673        5.27       62.74
             Antigua and Barbuda |      3,366        4.83       67.57
                          Belize |      3,305        4.74       72.32
           Saint Kitts and Nevis |      3,057        4.39       76.70
                      Cape Verde |      2,678        3.84       80.55
           Sao Tome and Principe |      2,651        3.81       84.35
                   Liechtenstein |      1,703        2.44       86.80
                      Seychelles |      1,423        2.04       88.84
                      San Marino |      1,327        1.90       90.75
                          Monaco |      1,315        1.89       92.63
                         Andorra |      1,279        1.84       94.47
          Bosnia and Herzegovina |        893        1.28       95.75
                        Maldives |        607        0.87       96.62
                         Lebanon |        578        0.83       97.45
      German Democratic Republic |        232        0.33       97.78
                        Zimbabwe |        230        0.33       98.11
                          Brunei |        203        0.29       98.41
                            Peru |        162        0.23       98.64
                        Cambodia |        119        0.17       98.81
                           Ghana |        113        0.16       98.97
                         Vanuatu |        104        0.15       99.12
                         Tunisia |        101        0.14       99.27
                        Zanzibar |         90        0.13       99.39
                          Kuwait |         79        0.11       99.51
                          Uganda |         70        0.10       99.61
                         Hungary |         51        0.07       99.68
                     Afghanistan |         42        0.06       99.74
                Marshall Islands |         30        0.04       99.78
                           Syria |         27        0.04       99.82
                        Kiribati |         23        0.03       99.86
                      Bangladesh |         16        0.02       99.88
                           Palau |         16        0.02       99.90
                           Tonga |         15        0.02       99.92
                          Tuvalu |         14        0.02       99.94
                           Nauru |         12        0.02       99.96
             Republic of Vietnam |         12        0.02       99.98
                      Micronesia |         11        0.02       99.99
                            Laos |          4        0.01      100.00
---------------------------------+-----------------------------------
                           Total |     69,671      100.00

. *small countries not matched
. 
. keep if _merge==3
(69697 observations deleted)

. drop abbrev _merge majpow

. 
. 
. foreach x of varlist mid fmid war region lngdppc lnlifedeer lncap lnmilper lnmilex lnirst lnenergy lnupop lntpop
>  lntottr lnlifepen lnacli {
  2. rename `x' `x'1
  3. }

. 
. rename ccode ccode1

. rename ccode2 ccode

. sort ccode year

. 
. merge m:1 ccode year using "outdatatotal1211176.dta"
ccode was int now double

    Result                           # of obs.
    -----------------------------------------
    not matched                        40,018
        from master                    39,966  (_merge==1)
        from using                         52  (_merge==2)

    matched                           436,541  (_merge==3)
    -----------------------------------------

. 
. keep if _merge==3
(40018 observations deleted)

. drop abbrev _merge majpow

. 
. rename ccode ccode2

. 
. foreach x of varlist mid fmid war region lngdppc lnlifedeer lncap lnmilper lnmilex lnirst lnenergy lnupop lntpop
>  lntottr lnlifepen lnacli {
  2. rename `x' `x'2
  3. }

. 
. gen lnlifedeerl=min(lnlifedeer1, lnlifedeer2) if lnlifedeer1~=. & lnlifedeer2~=.

. gen lngdppcl=min(lngdppc1, lngdppc2) if lngdppc1~=. & lngdppc2~=.

. gen lncapl=min(lncap1, lncap2) if lncap1~=. & lncap2~=.

. gen lnmilperl=min(lnmilper1, lnmilper2) if lnmilper1~=. & lnmilper2~=.

. gen lnmilexl=min(lnmilex1, lnmilex2) if lnmilex1~=. & lnmilex2~=.

. gen lnirstl=min(lnirst1, lnirst2) if lnirst1~=. & lnirst2~=.

. gen lnenergyl=min(lnenergy1, lnenergy2) if lnenergy1~=. & lnenergy2~=.

. gen lnupopl=min(lnupop1, lnupop2) if lnupop1~=. & lnupop2~=.

. gen lntpopl=min(lntpop1, lntpop2) if lntpop1~=. & lntpop2~=.

. gen lntottrl=min(lntottr1, lntottr2) if lntottr1~=. & lntottr2~=.

. gen lnlifepenl=min(lnlifepen1, lnlifepen2) if lnlifepen1~=. & lnlifepen2~=.

. gen lnaclil=min(lnacli1, lnacli2) if lnacli1~=. & lnacli2~=.

. 
. gen lnlifedeerh=max(lnlifedeer1, lnlifedeer2) if lnlifedeer1~=. & lnlifedeer2~=.

. gen lngdppch=max(lngdppc1, lngdppc2) if lngdppc1~=. & lngdppc2~=.

. gen lncaph=max(lncap1, lncap2) if lncap1~=. & lncap2~=.

. gen lnmilperh=max(lnmilper1, lnmilper2) if lnmilper1~=. & lnmilper2~=.

. gen lnmilexh=max(lnmilex1, lnmilex2) if lnmilex1~=. & lnmilex2~=.

. gen lnirsth=max(lnirst1, lnirst2) if lnirst1~=. & lnirst2~=.

. gen lnenergyh=max(lnenergy1, lnenergy2) if lnenergy1~=. & lnenergy2~=.

. gen lnupoph=max(lnupop1, lnupop2) if lnupop1~=. & lnupop2~=.

. gen lntpoph=max(lntpop1, lntpop2) if lntpop1~=. & lntpop2~=.

. gen lntottrh=max(lntottr1, lntottr2) if lntottr1~=. & lntottr2~=.

. gen lnlifepenh=max(lnlifepen1, lnlifepen2) if lnlifepen1~=. & lnlifepen2~=.

. gen lnaclih=max(lnacli1, lnacli2) if lnacli1~=. & lnacli2~=.

. 
. *simplify dataset:
. keep country_name1 country_name2 ccode1 ccode2 year abbrev1 abbrev2 cwmid cwongo cwfatald dyadid lncprt mjpw dml
>  dmh contigl lndist polity22 contig distance numstate numGPs cwongonm cwmidnm cwpceyrs midonsl midongl fmidonsl 
> fmidongl warl midyears midyears2 midyears3 fmidyears fmidyears2 fmidyears3 waryears waryears2 waryears3 tpop MMC
> IE lnlifedeerl lngdppcl lnlifepenl lnlifedeerh lngdppch lnlifepenh

. 
. count
436541

. 
. gen m=6

. 
. sort ccode1 ccode2 year

. save "mi_6.dta", replace
(note: file mi_6.dta not found)
file mi_6.dta saved

. 
. 
. 
. 
. *imputation 7
. clear

. use "12-05-28_dem_CIE_analysis.dta"

. 
. drop if year<1950
(0 observations deleted)

. drop if year>2001
(0 observations deleted)

. 
. rename ccode1 ccode

. sort ccode year

. merge m:1 ccode year using "outdatatotal1211177.dta"
ccode was int now double
year was int now double
numstate was int now long
numGPs was byte now long
polity2 was byte now long

    Result                           # of obs.
    -----------------------------------------
    not matched                        69,697
        from master                    69,671  (_merge==1)
        from using                         26  (_merge==2)

    matched                           476,507  (_merge==3)
    -----------------------------------------

. 
. tab country_name1 if _merge==1, sort

       COW CCode Num for state 1 |      Freq.     Percent        Cum.
---------------------------------+-----------------------------------
                      Luxembourg |      5,560        7.98        7.98
                        Barbados |      5,485        7.87       15.85
                         Bahamas |      4,776        6.86       22.71
                         Grenada |      4,418        6.34       29.05
                         Iceland |      4,173        5.99       35.04
                           Malta |      4,165        5.98       41.02
                        Dominica |      3,886        5.58       46.59
                        Suriname |      3,881        5.57       52.17
                     Saint Lucia |      3,696        5.30       57.47
Saint Vincent and the Grenadines |      3,673        5.27       62.74
             Antigua and Barbuda |      3,366        4.83       67.57
                          Belize |      3,305        4.74       72.32
           Saint Kitts and Nevis |      3,057        4.39       76.70
                      Cape Verde |      2,678        3.84       80.55
           Sao Tome and Principe |      2,651        3.81       84.35
                   Liechtenstein |      1,703        2.44       86.80
                      Seychelles |      1,423        2.04       88.84
                      San Marino |      1,327        1.90       90.75
                          Monaco |      1,315        1.89       92.63
                         Andorra |      1,279        1.84       94.47
          Bosnia and Herzegovina |        893        1.28       95.75
                        Maldives |        607        0.87       96.62
                         Lebanon |        578        0.83       97.45
      German Democratic Republic |        232        0.33       97.78
                        Zimbabwe |        230        0.33       98.11
                          Brunei |        203        0.29       98.41
                            Peru |        162        0.23       98.64
                        Cambodia |        119        0.17       98.81
                           Ghana |        113        0.16       98.97
                         Vanuatu |        104        0.15       99.12
                         Tunisia |        101        0.14       99.27
                        Zanzibar |         90        0.13       99.39
                          Kuwait |         79        0.11       99.51
                          Uganda |         70        0.10       99.61
                         Hungary |         51        0.07       99.68
                     Afghanistan |         42        0.06       99.74
                Marshall Islands |         30        0.04       99.78
                           Syria |         27        0.04       99.82
                        Kiribati |         23        0.03       99.86
                      Bangladesh |         16        0.02       99.88
                           Palau |         16        0.02       99.90
                           Tonga |         15        0.02       99.92
                          Tuvalu |         14        0.02       99.94
                           Nauru |         12        0.02       99.96
             Republic of Vietnam |         12        0.02       99.98
                      Micronesia |         11        0.02       99.99
                            Laos |          4        0.01      100.00
---------------------------------+-----------------------------------
                           Total |     69,671      100.00

. *small countries not matched
. 
. keep if _merge==3
(69697 observations deleted)

. drop abbrev _merge majpow

. 
. 
. foreach x of varlist mid fmid war region lngdppc lnlifedeer lncap lnmilper lnmilex lnirst lnenergy lnupop lntpop
>  lntottr lnlifepen lnacli {
  2. rename `x' `x'1
  3. }

. 
. rename ccode ccode1

. rename ccode2 ccode

. sort ccode year

. 
. merge m:1 ccode year using "outdatatotal1211177.dta"
ccode was int now double

    Result                           # of obs.
    -----------------------------------------
    not matched                        40,018
        from master                    39,966  (_merge==1)
        from using                         52  (_merge==2)

    matched                           436,541  (_merge==3)
    -----------------------------------------

. 
. keep if _merge==3
(40018 observations deleted)

. drop abbrev _merge majpow

. 
. rename ccode ccode2

. 
. foreach x of varlist mid fmid war region lngdppc lnlifedeer lncap lnmilper lnmilex lnirst lnenergy lnupop lntpop
>  lntottr lnlifepen lnacli {
  2. rename `x' `x'2
  3. }

. 
. gen lnlifedeerl=min(lnlifedeer1, lnlifedeer2) if lnlifedeer1~=. & lnlifedeer2~=.

. gen lngdppcl=min(lngdppc1, lngdppc2) if lngdppc1~=. & lngdppc2~=.

. gen lncapl=min(lncap1, lncap2) if lncap1~=. & lncap2~=.

. gen lnmilperl=min(lnmilper1, lnmilper2) if lnmilper1~=. & lnmilper2~=.

. gen lnmilexl=min(lnmilex1, lnmilex2) if lnmilex1~=. & lnmilex2~=.

. gen lnirstl=min(lnirst1, lnirst2) if lnirst1~=. & lnirst2~=.

. gen lnenergyl=min(lnenergy1, lnenergy2) if lnenergy1~=. & lnenergy2~=.

. gen lnupopl=min(lnupop1, lnupop2) if lnupop1~=. & lnupop2~=.

. gen lntpopl=min(lntpop1, lntpop2) if lntpop1~=. & lntpop2~=.

. gen lntottrl=min(lntottr1, lntottr2) if lntottr1~=. & lntottr2~=.

. gen lnlifepenl=min(lnlifepen1, lnlifepen2) if lnlifepen1~=. & lnlifepen2~=.

. gen lnaclil=min(lnacli1, lnacli2) if lnacli1~=. & lnacli2~=.

. 
. gen lnlifedeerh=max(lnlifedeer1, lnlifedeer2) if lnlifedeer1~=. & lnlifedeer2~=.

. gen lngdppch=max(lngdppc1, lngdppc2) if lngdppc1~=. & lngdppc2~=.

. gen lncaph=max(lncap1, lncap2) if lncap1~=. & lncap2~=.

. gen lnmilperh=max(lnmilper1, lnmilper2) if lnmilper1~=. & lnmilper2~=.

. gen lnmilexh=max(lnmilex1, lnmilex2) if lnmilex1~=. & lnmilex2~=.

. gen lnirsth=max(lnirst1, lnirst2) if lnirst1~=. & lnirst2~=.

. gen lnenergyh=max(lnenergy1, lnenergy2) if lnenergy1~=. & lnenergy2~=.

. gen lnupoph=max(lnupop1, lnupop2) if lnupop1~=. & lnupop2~=.

. gen lntpoph=max(lntpop1, lntpop2) if lntpop1~=. & lntpop2~=.

. gen lntottrh=max(lntottr1, lntottr2) if lntottr1~=. & lntottr2~=.

. gen lnlifepenh=max(lnlifepen1, lnlifepen2) if lnlifepen1~=. & lnlifepen2~=.

. gen lnaclih=max(lnacli1, lnacli2) if lnacli1~=. & lnacli2~=.

. 
. *simplify dataset:
. keep country_name1 country_name2 ccode1 ccode2 year abbrev1 abbrev2 cwmid cwongo cwfatald dyadid lncprt mjpw dml
>  dmh contigl lndist polity22 contig distance numstate numGPs cwongonm cwmidnm cwpceyrs midonsl midongl fmidonsl 
> fmidongl warl midyears midyears2 midyears3 fmidyears fmidyears2 fmidyears3 waryears waryears2 waryears3 tpop MMC
> IE lnlifedeerl lngdppcl lnlifepenl lnlifedeerh lngdppch lnlifepenh

. 
. count
436541

. 
. gen m=7

. 
. sort ccode1 ccode2 year

. save "mi_7.dta", replace
(note: file mi_7.dta not found)
file mi_7.dta saved

. 
. 
. 
. 
. *imputation 8
. clear

. use "12-05-28_dem_CIE_analysis.dta"

. 
. drop if year<1950
(0 observations deleted)

. drop if year>2001
(0 observations deleted)

. 
. rename ccode1 ccode

. sort ccode year

. merge m:1 ccode year using "outdatatotal1211178.dta"
ccode was int now double
year was int now double
numstate was int now long
numGPs was byte now long
polity2 was byte now long

    Result                           # of obs.
    -----------------------------------------
    not matched                        69,697
        from master                    69,671  (_merge==1)
        from using                         26  (_merge==2)

    matched                           476,507  (_merge==3)
    -----------------------------------------

. 
. tab country_name1 if _merge==1, sort

       COW CCode Num for state 1 |      Freq.     Percent        Cum.
---------------------------------+-----------------------------------
                      Luxembourg |      5,560        7.98        7.98
                        Barbados |      5,485        7.87       15.85
                         Bahamas |      4,776        6.86       22.71
                         Grenada |      4,418        6.34       29.05
                         Iceland |      4,173        5.99       35.04
                           Malta |      4,165        5.98       41.02
                        Dominica |      3,886        5.58       46.59
                        Suriname |      3,881        5.57       52.17
                     Saint Lucia |      3,696        5.30       57.47
Saint Vincent and the Grenadines |      3,673        5.27       62.74
             Antigua and Barbuda |      3,366        4.83       67.57
                          Belize |      3,305        4.74       72.32
           Saint Kitts and Nevis |      3,057        4.39       76.70
                      Cape Verde |      2,678        3.84       80.55
           Sao Tome and Principe |      2,651        3.81       84.35
                   Liechtenstein |      1,703        2.44       86.80
                      Seychelles |      1,423        2.04       88.84
                      San Marino |      1,327        1.90       90.75
                          Monaco |      1,315        1.89       92.63
                         Andorra |      1,279        1.84       94.47
          Bosnia and Herzegovina |        893        1.28       95.75
                        Maldives |        607        0.87       96.62
                         Lebanon |        578        0.83       97.45
      German Democratic Republic |        232        0.33       97.78
                        Zimbabwe |        230        0.33       98.11
                          Brunei |        203        0.29       98.41
                            Peru |        162        0.23       98.64
                        Cambodia |        119        0.17       98.81
                           Ghana |        113        0.16       98.97
                         Vanuatu |        104        0.15       99.12
                         Tunisia |        101        0.14       99.27
                        Zanzibar |         90        0.13       99.39
                          Kuwait |         79        0.11       99.51
                          Uganda |         70        0.10       99.61
                         Hungary |         51        0.07       99.68
                     Afghanistan |         42        0.06       99.74
                Marshall Islands |         30        0.04       99.78
                           Syria |         27        0.04       99.82
                        Kiribati |         23        0.03       99.86
                      Bangladesh |         16        0.02       99.88
                           Palau |         16        0.02       99.90
                           Tonga |         15        0.02       99.92
                          Tuvalu |         14        0.02       99.94
                           Nauru |         12        0.02       99.96
             Republic of Vietnam |         12        0.02       99.98
                      Micronesia |         11        0.02       99.99
                            Laos |          4        0.01      100.00
---------------------------------+-----------------------------------
                           Total |     69,671      100.00

. *small countries not matched
. 
. keep if _merge==3
(69697 observations deleted)

. drop abbrev _merge majpow

. 
. 
. foreach x of varlist mid fmid war region lngdppc lnlifedeer lncap lnmilper lnmilex lnirst lnenergy lnupop lntpop
>  lntottr lnlifepen lnacli {
  2. rename `x' `x'1
  3. }

. 
. rename ccode ccode1

. rename ccode2 ccode

. sort ccode year

. 
. merge m:1 ccode year using "outdatatotal1211178.dta"
ccode was int now double

    Result                           # of obs.
    -----------------------------------------
    not matched                        40,018
        from master                    39,966  (_merge==1)
        from using                         52  (_merge==2)

    matched                           436,541  (_merge==3)
    -----------------------------------------

. 
. keep if _merge==3
(40018 observations deleted)

. drop abbrev _merge majpow

. 
. rename ccode ccode2

. 
. foreach x of varlist mid fmid war region lngdppc lnlifedeer lncap lnmilper lnmilex lnirst lnenergy lnupop lntpop
>  lntottr lnlifepen lnacli {
  2. rename `x' `x'2
  3. }

. 
. gen lnlifedeerl=min(lnlifedeer1, lnlifedeer2) if lnlifedeer1~=. & lnlifedeer2~=.

. gen lngdppcl=min(lngdppc1, lngdppc2) if lngdppc1~=. & lngdppc2~=.

. gen lncapl=min(lncap1, lncap2) if lncap1~=. & lncap2~=.

. gen lnmilperl=min(lnmilper1, lnmilper2) if lnmilper1~=. & lnmilper2~=.

. gen lnmilexl=min(lnmilex1, lnmilex2) if lnmilex1~=. & lnmilex2~=.

. gen lnirstl=min(lnirst1, lnirst2) if lnirst1~=. & lnirst2~=.

. gen lnenergyl=min(lnenergy1, lnenergy2) if lnenergy1~=. & lnenergy2~=.

. gen lnupopl=min(lnupop1, lnupop2) if lnupop1~=. & lnupop2~=.

. gen lntpopl=min(lntpop1, lntpop2) if lntpop1~=. & lntpop2~=.

. gen lntottrl=min(lntottr1, lntottr2) if lntottr1~=. & lntottr2~=.

. gen lnlifepenl=min(lnlifepen1, lnlifepen2) if lnlifepen1~=. & lnlifepen2~=.

. gen lnaclil=min(lnacli1, lnacli2) if lnacli1~=. & lnacli2~=.

. 
. gen lnlifedeerh=max(lnlifedeer1, lnlifedeer2) if lnlifedeer1~=. & lnlifedeer2~=.

. gen lngdppch=max(lngdppc1, lngdppc2) if lngdppc1~=. & lngdppc2~=.

. gen lncaph=max(lncap1, lncap2) if lncap1~=. & lncap2~=.

. gen lnmilperh=max(lnmilper1, lnmilper2) if lnmilper1~=. & lnmilper2~=.

. gen lnmilexh=max(lnmilex1, lnmilex2) if lnmilex1~=. & lnmilex2~=.

. gen lnirsth=max(lnirst1, lnirst2) if lnirst1~=. & lnirst2~=.

. gen lnenergyh=max(lnenergy1, lnenergy2) if lnenergy1~=. & lnenergy2~=.

. gen lnupoph=max(lnupop1, lnupop2) if lnupop1~=. & lnupop2~=.

. gen lntpoph=max(lntpop1, lntpop2) if lntpop1~=. & lntpop2~=.

. gen lntottrh=max(lntottr1, lntottr2) if lntottr1~=. & lntottr2~=.

. gen lnlifepenh=max(lnlifepen1, lnlifepen2) if lnlifepen1~=. & lnlifepen2~=.

. gen lnaclih=max(lnacli1, lnacli2) if lnacli1~=. & lnacli2~=.

. 
. *simplify dataset:
. keep country_name1 country_name2 ccode1 ccode2 year abbrev1 abbrev2 cwmid cwongo cwfatald dyadid lncprt mjpw dml
>  dmh contigl lndist polity22 contig distance numstate numGPs cwongonm cwmidnm cwpceyrs midonsl midongl fmidonsl 
> fmidongl warl midyears midyears2 midyears3 fmidyears fmidyears2 fmidyears3 waryears waryears2 waryears3 tpop MMC
> IE lnlifedeerl lngdppcl lnlifepenl lnlifedeerh lngdppch lnlifepenh

. 
. count
436541

. 
. gen m=8

. 
. sort ccode1 ccode2 year

. save "mi_8.dta", replace
(note: file mi_8.dta not found)
file mi_8.dta saved

. 
. 
. 
. *imputation 9
. clear

. use "12-05-28_dem_CIE_analysis.dta"

. 
. drop if year<1950
(0 observations deleted)

. drop if year>2001
(0 observations deleted)

. 
. rename ccode1 ccode

. sort ccode year

. merge m:1 ccode year using "outdatatotal1211179.dta"
ccode was int now double
year was int now double
numstate was int now long
numGPs was byte now long
polity2 was byte now long

    Result                           # of obs.
    -----------------------------------------
    not matched                        69,697
        from master                    69,671  (_merge==1)
        from using                         26  (_merge==2)

    matched                           476,507  (_merge==3)
    -----------------------------------------

. 
. tab country_name1 if _merge==1, sort

       COW CCode Num for state 1 |      Freq.     Percent        Cum.
---------------------------------+-----------------------------------
                      Luxembourg |      5,560        7.98        7.98
                        Barbados |      5,485        7.87       15.85
                         Bahamas |      4,776        6.86       22.71
                         Grenada |      4,418        6.34       29.05
                         Iceland |      4,173        5.99       35.04
                           Malta |      4,165        5.98       41.02
                        Dominica |      3,886        5.58       46.59
                        Suriname |      3,881        5.57       52.17
                     Saint Lucia |      3,696        5.30       57.47
Saint Vincent and the Grenadines |      3,673        5.27       62.74
             Antigua and Barbuda |      3,366        4.83       67.57
                          Belize |      3,305        4.74       72.32
           Saint Kitts and Nevis |      3,057        4.39       76.70
                      Cape Verde |      2,678        3.84       80.55
           Sao Tome and Principe |      2,651        3.81       84.35
                   Liechtenstein |      1,703        2.44       86.80
                      Seychelles |      1,423        2.04       88.84
                      San Marino |      1,327        1.90       90.75
                          Monaco |      1,315        1.89       92.63
                         Andorra |      1,279        1.84       94.47
          Bosnia and Herzegovina |        893        1.28       95.75
                        Maldives |        607        0.87       96.62
                         Lebanon |        578        0.83       97.45
      German Democratic Republic |        232        0.33       97.78
                        Zimbabwe |        230        0.33       98.11
                          Brunei |        203        0.29       98.41
                            Peru |        162        0.23       98.64
                        Cambodia |        119        0.17       98.81
                           Ghana |        113        0.16       98.97
                         Vanuatu |        104        0.15       99.12
                         Tunisia |        101        0.14       99.27
                        Zanzibar |         90        0.13       99.39
                          Kuwait |         79        0.11       99.51
                          Uganda |         70        0.10       99.61
                         Hungary |         51        0.07       99.68
                     Afghanistan |         42        0.06       99.74
                Marshall Islands |         30        0.04       99.78
                           Syria |         27        0.04       99.82
                        Kiribati |         23        0.03       99.86
                      Bangladesh |         16        0.02       99.88
                           Palau |         16        0.02       99.90
                           Tonga |         15        0.02       99.92
                          Tuvalu |         14        0.02       99.94
                           Nauru |         12        0.02       99.96
             Republic of Vietnam |         12        0.02       99.98
                      Micronesia |         11        0.02       99.99
                            Laos |          4        0.01      100.00
---------------------------------+-----------------------------------
                           Total |     69,671      100.00

. *small countries not matched
. 
. keep if _merge==3
(69697 observations deleted)

. drop abbrev _merge majpow

. 
. 
. foreach x of varlist mid fmid war region lngdppc lnlifedeer lncap lnmilper lnmilex lnirst lnenergy lnupop lntpop
>  lntottr lnlifepen lnacli {
  2. rename `x' `x'1
  3. }

. 
. rename ccode ccode1

. rename ccode2 ccode

. sort ccode year

. 
. merge m:1 ccode year using "outdatatotal1211179.dta"
ccode was int now double

    Result                           # of obs.
    -----------------------------------------
    not matched                        40,018
        from master                    39,966  (_merge==1)
        from using                         52  (_merge==2)

    matched                           436,541  (_merge==3)
    -----------------------------------------

. 
. keep if _merge==3
(40018 observations deleted)

. drop abbrev _merge majpow

. 
. rename ccode ccode2

. 
. foreach x of varlist mid fmid war region lngdppc lnlifedeer lncap lnmilper lnmilex lnirst lnenergy lnupop lntpop
>  lntottr lnlifepen lnacli {
  2. rename `x' `x'2
  3. }

. 
. gen lnlifedeerl=min(lnlifedeer1, lnlifedeer2) if lnlifedeer1~=. & lnlifedeer2~=.

. gen lngdppcl=min(lngdppc1, lngdppc2) if lngdppc1~=. & lngdppc2~=.

. gen lncapl=min(lncap1, lncap2) if lncap1~=. & lncap2~=.

. gen lnmilperl=min(lnmilper1, lnmilper2) if lnmilper1~=. & lnmilper2~=.

. gen lnmilexl=min(lnmilex1, lnmilex2) if lnmilex1~=. & lnmilex2~=.

. gen lnirstl=min(lnirst1, lnirst2) if lnirst1~=. & lnirst2~=.

. gen lnenergyl=min(lnenergy1, lnenergy2) if lnenergy1~=. & lnenergy2~=.

. gen lnupopl=min(lnupop1, lnupop2) if lnupop1~=. & lnupop2~=.

. gen lntpopl=min(lntpop1, lntpop2) if lntpop1~=. & lntpop2~=.

. gen lntottrl=min(lntottr1, lntottr2) if lntottr1~=. & lntottr2~=.

. gen lnlifepenl=min(lnlifepen1, lnlifepen2) if lnlifepen1~=. & lnlifepen2~=.

. gen lnaclil=min(lnacli1, lnacli2) if lnacli1~=. & lnacli2~=.

. 
. gen lnlifedeerh=max(lnlifedeer1, lnlifedeer2) if lnlifedeer1~=. & lnlifedeer2~=.

. gen lngdppch=max(lngdppc1, lngdppc2) if lngdppc1~=. & lngdppc2~=.

. gen lncaph=max(lncap1, lncap2) if lncap1~=. & lncap2~=.

. gen lnmilperh=max(lnmilper1, lnmilper2) if lnmilper1~=. & lnmilper2~=.

. gen lnmilexh=max(lnmilex1, lnmilex2) if lnmilex1~=. & lnmilex2~=.

. gen lnirsth=max(lnirst1, lnirst2) if lnirst1~=. & lnirst2~=.

. gen lnenergyh=max(lnenergy1, lnenergy2) if lnenergy1~=. & lnenergy2~=.

. gen lnupoph=max(lnupop1, lnupop2) if lnupop1~=. & lnupop2~=.

. gen lntpoph=max(lntpop1, lntpop2) if lntpop1~=. & lntpop2~=.

. gen lntottrh=max(lntottr1, lntottr2) if lntottr1~=. & lntottr2~=.

. gen lnlifepenh=max(lnlifepen1, lnlifepen2) if lnlifepen1~=. & lnlifepen2~=.

. gen lnaclih=max(lnacli1, lnacli2) if lnacli1~=. & lnacli2~=.

. 
. *simplify dataset:
. keep country_name1 country_name2 ccode1 ccode2 year abbrev1 abbrev2 cwmid cwongo cwfatald dyadid lncprt mjpw dml
>  dmh contigl lndist polity22 contig distance numstate numGPs cwongonm cwmidnm cwpceyrs midonsl midongl fmidonsl 
> fmidongl warl midyears midyears2 midyears3 fmidyears fmidyears2 fmidyears3 waryears waryears2 waryears3 tpop MMC
> IE lnlifedeerl lngdppcl lnlifepenl lnlifedeerh lngdppch lnlifepenh

. 
. count
436541

. 
. gen m=9

. 
. sort ccode1 ccode2 year

. save "mi_9.dta", replace
(note: file mi_9.dta not found)
file mi_9.dta saved

. 
. 
. 
. 
. 
. *imputation 10
. clear

. use "12-05-28_dem_CIE_analysis.dta"

. 
. drop if year<1950
(0 observations deleted)

. drop if year>2001
(0 observations deleted)

. 
. rename ccode1 ccode

. sort ccode year

. merge m:1 ccode year using "outdatatotal12111710.dta"
ccode was int now double
year was int now double
numstate was int now long
numGPs was byte now long
polity2 was byte now long

    Result                           # of obs.
    -----------------------------------------
    not matched                        69,697
        from master                    69,671  (_merge==1)
        from using                         26  (_merge==2)

    matched                           476,507  (_merge==3)
    -----------------------------------------

. 
. tab country_name1 if _merge==1, sort

       COW CCode Num for state 1 |      Freq.     Percent        Cum.
---------------------------------+-----------------------------------
                      Luxembourg |      5,560        7.98        7.98
                        Barbados |      5,485        7.87       15.85
                         Bahamas |      4,776        6.86       22.71
                         Grenada |      4,418        6.34       29.05
                         Iceland |      4,173        5.99       35.04
                           Malta |      4,165        5.98       41.02
                        Dominica |      3,886        5.58       46.59
                        Suriname |      3,881        5.57       52.17
                     Saint Lucia |      3,696        5.30       57.47
Saint Vincent and the Grenadines |      3,673        5.27       62.74
             Antigua and Barbuda |      3,366        4.83       67.57
                          Belize |      3,305        4.74       72.32
           Saint Kitts and Nevis |      3,057        4.39       76.70
                      Cape Verde |      2,678        3.84       80.55
           Sao Tome and Principe |      2,651        3.81       84.35
                   Liechtenstein |      1,703        2.44       86.80
                      Seychelles |      1,423        2.04       88.84
                      San Marino |      1,327        1.90       90.75
                          Monaco |      1,315        1.89       92.63
                         Andorra |      1,279        1.84       94.47
          Bosnia and Herzegovina |        893        1.28       95.75
                        Maldives |        607        0.87       96.62
                         Lebanon |        578        0.83       97.45
      German Democratic Republic |        232        0.33       97.78
                        Zimbabwe |        230        0.33       98.11
                          Brunei |        203        0.29       98.41
                            Peru |        162        0.23       98.64
                        Cambodia |        119        0.17       98.81
                           Ghana |        113        0.16       98.97
                         Vanuatu |        104        0.15       99.12
                         Tunisia |        101        0.14       99.27
                        Zanzibar |         90        0.13       99.39
                          Kuwait |         79        0.11       99.51
                          Uganda |         70        0.10       99.61
                         Hungary |         51        0.07       99.68
                     Afghanistan |         42        0.06       99.74
                Marshall Islands |         30        0.04       99.78
                           Syria |         27        0.04       99.82
                        Kiribati |         23        0.03       99.86
                      Bangladesh |         16        0.02       99.88
                           Palau |         16        0.02       99.90
                           Tonga |         15        0.02       99.92
                          Tuvalu |         14        0.02       99.94
                           Nauru |         12        0.02       99.96
             Republic of Vietnam |         12        0.02       99.98
                      Micronesia |         11        0.02       99.99
                            Laos |          4        0.01      100.00
---------------------------------+-----------------------------------
                           Total |     69,671      100.00

. *small countries not matched
. 
. keep if _merge==3
(69697 observations deleted)

. drop abbrev _merge majpow

. 
. 
. foreach x of varlist mid fmid war region lngdppc lnlifedeer lncap lnmilper lnmilex lnirst lnenergy lnupop lntpop
>  lntottr lnlifepen lnacli {
  2. rename `x' `x'1
  3. }

. 
. rename ccode ccode1

. rename ccode2 ccode

. sort ccode year

. 
. merge m:1 ccode year using "outdatatotal12111710.dta"
ccode was int now double

    Result                           # of obs.
    -----------------------------------------
    not matched                        40,018
        from master                    39,966  (_merge==1)
        from using                         52  (_merge==2)

    matched                           436,541  (_merge==3)
    -----------------------------------------

. 
. keep if _merge==3
(40018 observations deleted)

. drop abbrev _merge majpow

. 
. rename ccode ccode2

. 
. foreach x of varlist mid fmid war region lngdppc lnlifedeer lncap lnmilper lnmilex lnirst lnenergy lnupop lntpop
>  lntottr lnlifepen lnacli {
  2. rename `x' `x'2
  3. }

. 
. gen lnlifedeerl=min(lnlifedeer1, lnlifedeer2) if lnlifedeer1~=. & lnlifedeer2~=.

. gen lngdppcl=min(lngdppc1, lngdppc2) if lngdppc1~=. & lngdppc2~=.

. gen lncapl=min(lncap1, lncap2) if lncap1~=. & lncap2~=.

. gen lnmilperl=min(lnmilper1, lnmilper2) if lnmilper1~=. & lnmilper2~=.

. gen lnmilexl=min(lnmilex1, lnmilex2) if lnmilex1~=. & lnmilex2~=.

. gen lnirstl=min(lnirst1, lnirst2) if lnirst1~=. & lnirst2~=.

. gen lnenergyl=min(lnenergy1, lnenergy2) if lnenergy1~=. & lnenergy2~=.

. gen lnupopl=min(lnupop1, lnupop2) if lnupop1~=. & lnupop2~=.

. gen lntpopl=min(lntpop1, lntpop2) if lntpop1~=. & lntpop2~=.

. gen lntottrl=min(lntottr1, lntottr2) if lntottr1~=. & lntottr2~=.

. gen lnlifepenl=min(lnlifepen1, lnlifepen2) if lnlifepen1~=. & lnlifepen2~=.

. gen lnaclil=min(lnacli1, lnacli2) if lnacli1~=. & lnacli2~=.

. 
. gen lnlifedeerh=max(lnlifedeer1, lnlifedeer2) if lnlifedeer1~=. & lnlifedeer2~=.

. gen lngdppch=max(lngdppc1, lngdppc2) if lngdppc1~=. & lngdppc2~=.

. gen lncaph=max(lncap1, lncap2) if lncap1~=. & lncap2~=.

. gen lnmilperh=max(lnmilper1, lnmilper2) if lnmilper1~=. & lnmilper2~=.

. gen lnmilexh=max(lnmilex1, lnmilex2) if lnmilex1~=. & lnmilex2~=.

. gen lnirsth=max(lnirst1, lnirst2) if lnirst1~=. & lnirst2~=.

. gen lnenergyh=max(lnenergy1, lnenergy2) if lnenergy1~=. & lnenergy2~=.

. gen lnupoph=max(lnupop1, lnupop2) if lnupop1~=. & lnupop2~=.

. gen lntpoph=max(lntpop1, lntpop2) if lntpop1~=. & lntpop2~=.

. gen lntottrh=max(lntottr1, lntottr2) if lntottr1~=. & lntottr2~=.

. gen lnlifepenh=max(lnlifepen1, lnlifepen2) if lnlifepen1~=. & lnlifepen2~=.

. gen lnaclih=max(lnacli1, lnacli2) if lnacli1~=. & lnacli2~=.

. 
. *simplify dataset:
. keep country_name1 country_name2 ccode1 ccode2 year abbrev1 abbrev2 cwmid cwongo cwfatald dyadid lncprt mjpw dml
>  dmh contigl lndist polity22 contig distance numstate numGPs cwongonm cwmidnm cwpceyrs midonsl midongl fmidonsl 
> fmidongl warl midyears midyears2 midyears3 fmidyears fmidyears2 fmidyears3 waryears waryears2 waryears3 tpop MMC
> IE lnlifedeerl lngdppcl lnlifepenl lnlifedeerh lngdppch lnlifepenh

. 
. count
436541

. 
. gen m=10

. 
. sort ccode1 ccode2 year

. save "mi_10.dta", replace
(note: file mi_10.dta not found)
file mi_10.dta saved

. 
. 
. 
. 
. 
. 
. 
. 
. *imputation 11
. clear

. use "12-05-28_dem_CIE_analysis.dta"

. 
. drop if year<1950
(0 observations deleted)

. drop if year>2001
(0 observations deleted)

. 
. rename ccode1 ccode

. sort ccode year

. merge m:1 ccode year using "outdatatotal12111711.dta"
ccode was int now double
year was int now double
numstate was int now long
numGPs was byte now long
polity2 was byte now long

    Result                           # of obs.
    -----------------------------------------
    not matched                        69,697
        from master                    69,671  (_merge==1)
        from using                         26  (_merge==2)

    matched                           476,507  (_merge==3)
    -----------------------------------------

. 
. tab country_name1 if _merge==1, sort

       COW CCode Num for state 1 |      Freq.     Percent        Cum.
---------------------------------+-----------------------------------
                      Luxembourg |      5,560        7.98        7.98
                        Barbados |      5,485        7.87       15.85
                         Bahamas |      4,776        6.86       22.71
                         Grenada |      4,418        6.34       29.05
                         Iceland |      4,173        5.99       35.04
                           Malta |      4,165        5.98       41.02
                        Dominica |      3,886        5.58       46.59
                        Suriname |      3,881        5.57       52.17
                     Saint Lucia |      3,696        5.30       57.47
Saint Vincent and the Grenadines |      3,673        5.27       62.74
             Antigua and Barbuda |      3,366        4.83       67.57
                          Belize |      3,305        4.74       72.32
           Saint Kitts and Nevis |      3,057        4.39       76.70
                      Cape Verde |      2,678        3.84       80.55
           Sao Tome and Principe |      2,651        3.81       84.35
                   Liechtenstein |      1,703        2.44       86.80
                      Seychelles |      1,423        2.04       88.84
                      San Marino |      1,327        1.90       90.75
                          Monaco |      1,315        1.89       92.63
                         Andorra |      1,279        1.84       94.47
          Bosnia and Herzegovina |        893        1.28       95.75
                        Maldives |        607        0.87       96.62
                         Lebanon |        578        0.83       97.45
      German Democratic Republic |        232        0.33       97.78
                        Zimbabwe |        230        0.33       98.11
                          Brunei |        203        0.29       98.41
                            Peru |        162        0.23       98.64
                        Cambodia |        119        0.17       98.81
                           Ghana |        113        0.16       98.97
                         Vanuatu |        104        0.15       99.12
                         Tunisia |        101        0.14       99.27
                        Zanzibar |         90        0.13       99.39
                          Kuwait |         79        0.11       99.51
                          Uganda |         70        0.10       99.61
                         Hungary |         51        0.07       99.68
                     Afghanistan |         42        0.06       99.74
                Marshall Islands |         30        0.04       99.78
                           Syria |         27        0.04       99.82
                        Kiribati |         23        0.03       99.86
                      Bangladesh |         16        0.02       99.88
                           Palau |         16        0.02       99.90
                           Tonga |         15        0.02       99.92
                          Tuvalu |         14        0.02       99.94
                           Nauru |         12        0.02       99.96
             Republic of Vietnam |         12        0.02       99.98
                      Micronesia |         11        0.02       99.99
                            Laos |          4        0.01      100.00
---------------------------------+-----------------------------------
                           Total |     69,671      100.00

. *small countries not matched
. 
. keep if _merge==3
(69697 observations deleted)

. drop abbrev _merge majpow

. 
. 
. foreach x of varlist mid fmid war region lngdppc lnlifedeer lncap lnmilper lnmilex lnirst lnenergy lnupop lntpop
>  lntottr lnlifepen lnacli {
  2. rename `x' `x'1
  3. }

. 
. rename ccode ccode1

. rename ccode2 ccode

. sort ccode year

. 
. merge m:1 ccode year using "outdatatotal12111711.dta"
ccode was int now double

    Result                           # of obs.
    -----------------------------------------
    not matched                        40,018
        from master                    39,966  (_merge==1)
        from using                         52  (_merge==2)

    matched                           436,541  (_merge==3)
    -----------------------------------------

. 
. keep if _merge==3
(40018 observations deleted)

. drop abbrev _merge majpow

. 
. rename ccode ccode2

. 
. foreach x of varlist mid fmid war region lngdppc lnlifedeer lncap lnmilper lnmilex lnirst lnenergy lnupop lntpop
>  lntottr lnlifepen lnacli {
  2. rename `x' `x'2
  3. }

. 
. gen lnlifedeerl=min(lnlifedeer1, lnlifedeer2) if lnlifedeer1~=. & lnlifedeer2~=.

. gen lngdppcl=min(lngdppc1, lngdppc2) if lngdppc1~=. & lngdppc2~=.

. gen lncapl=min(lncap1, lncap2) if lncap1~=. & lncap2~=.

. gen lnmilperl=min(lnmilper1, lnmilper2) if lnmilper1~=. & lnmilper2~=.

. gen lnmilexl=min(lnmilex1, lnmilex2) if lnmilex1~=. & lnmilex2~=.

. gen lnirstl=min(lnirst1, lnirst2) if lnirst1~=. & lnirst2~=.

. gen lnenergyl=min(lnenergy1, lnenergy2) if lnenergy1~=. & lnenergy2~=.

. gen lnupopl=min(lnupop1, lnupop2) if lnupop1~=. & lnupop2~=.

. gen lntpopl=min(lntpop1, lntpop2) if lntpop1~=. & lntpop2~=.

. gen lntottrl=min(lntottr1, lntottr2) if lntottr1~=. & lntottr2~=.

. gen lnlifepenl=min(lnlifepen1, lnlifepen2) if lnlifepen1~=. & lnlifepen2~=.

. gen lnaclil=min(lnacli1, lnacli2) if lnacli1~=. & lnacli2~=.

. 
. gen lnlifedeerh=max(lnlifedeer1, lnlifedeer2) if lnlifedeer1~=. & lnlifedeer2~=.

. gen lngdppch=max(lngdppc1, lngdppc2) if lngdppc1~=. & lngdppc2~=.

. gen lncaph=max(lncap1, lncap2) if lncap1~=. & lncap2~=.

. gen lnmilperh=max(lnmilper1, lnmilper2) if lnmilper1~=. & lnmilper2~=.

. gen lnmilexh=max(lnmilex1, lnmilex2) if lnmilex1~=. & lnmilex2~=.

. gen lnirsth=max(lnirst1, lnirst2) if lnirst1~=. & lnirst2~=.

. gen lnenergyh=max(lnenergy1, lnenergy2) if lnenergy1~=. & lnenergy2~=.

. gen lnupoph=max(lnupop1, lnupop2) if lnupop1~=. & lnupop2~=.

. gen lntpoph=max(lntpop1, lntpop2) if lntpop1~=. & lntpop2~=.

. gen lntottrh=max(lntottr1, lntottr2) if lntottr1~=. & lntottr2~=.

. gen lnlifepenh=max(lnlifepen1, lnlifepen2) if lnlifepen1~=. & lnlifepen2~=.

. gen lnaclih=max(lnacli1, lnacli2) if lnacli1~=. & lnacli2~=.

. 
. *simplify dataset:
. keep country_name1 country_name2 ccode1 ccode2 year abbrev1 abbrev2 cwmid cwongo cwfatald dyadid lncprt mjpw dml
>  dmh contigl lndist polity22 contig distance numstate numGPs cwongonm cwmidnm cwpceyrs midonsl midongl fmidonsl 
> fmidongl warl midyears midyears2 midyears3 fmidyears fmidyears2 fmidyears3 waryears waryears2 waryears3 tpop MMC
> IE lnlifedeerl lngdppcl lnlifepenl lnlifedeerh lngdppch lnlifepenh

. 
. count
436541

. 
. gen m=11

. 
. sort ccode1 ccode2 year

. save "mi_11.dta", replace
(note: file mi_11.dta not found)
file mi_11.dta saved

. 
. 
. *imputation 12
. clear

. use "12-05-28_dem_CIE_analysis.dta"

. 
. drop if year<1950
(0 observations deleted)

. drop if year>2001
(0 observations deleted)

. 
. rename ccode1 ccode

. sort ccode year

. merge m:1 ccode year using "outdatatotal12111712.dta"
ccode was int now double
year was int now double
numstate was int now long
numGPs was byte now long
polity2 was byte now long

    Result                           # of obs.
    -----------------------------------------
    not matched                        69,697
        from master                    69,671  (_merge==1)
        from using                         26  (_merge==2)

    matched                           476,507  (_merge==3)
    -----------------------------------------

. 
. tab country_name1 if _merge==1, sort

       COW CCode Num for state 1 |      Freq.     Percent        Cum.
---------------------------------+-----------------------------------
                      Luxembourg |      5,560        7.98        7.98
                        Barbados |      5,485        7.87       15.85
                         Bahamas |      4,776        6.86       22.71
                         Grenada |      4,418        6.34       29.05
                         Iceland |      4,173        5.99       35.04
                           Malta |      4,165        5.98       41.02
                        Dominica |      3,886        5.58       46.59
                        Suriname |      3,881        5.57       52.17
                     Saint Lucia |      3,696        5.30       57.47
Saint Vincent and the Grenadines |      3,673        5.27       62.74
             Antigua and Barbuda |      3,366        4.83       67.57
                          Belize |      3,305        4.74       72.32
           Saint Kitts and Nevis |      3,057        4.39       76.70
                      Cape Verde |      2,678        3.84       80.55
           Sao Tome and Principe |      2,651        3.81       84.35
                   Liechtenstein |      1,703        2.44       86.80
                      Seychelles |      1,423        2.04       88.84
                      San Marino |      1,327        1.90       90.75
                          Monaco |      1,315        1.89       92.63
                         Andorra |      1,279        1.84       94.47
          Bosnia and Herzegovina |        893        1.28       95.75
                        Maldives |        607        0.87       96.62
                         Lebanon |        578        0.83       97.45
      German Democratic Republic |        232        0.33       97.78
                        Zimbabwe |        230        0.33       98.11
                          Brunei |        203        0.29       98.41
                            Peru |        162        0.23       98.64
                        Cambodia |        119        0.17       98.81
                           Ghana |        113        0.16       98.97
                         Vanuatu |        104        0.15       99.12
                         Tunisia |        101        0.14       99.27
                        Zanzibar |         90        0.13       99.39
                          Kuwait |         79        0.11       99.51
                          Uganda |         70        0.10       99.61
                         Hungary |         51        0.07       99.68
                     Afghanistan |         42        0.06       99.74
                Marshall Islands |         30        0.04       99.78
                           Syria |         27        0.04       99.82
                        Kiribati |         23        0.03       99.86
                      Bangladesh |         16        0.02       99.88
                           Palau |         16        0.02       99.90
                           Tonga |         15        0.02       99.92
                          Tuvalu |         14        0.02       99.94
                           Nauru |         12        0.02       99.96
             Republic of Vietnam |         12        0.02       99.98
                      Micronesia |         11        0.02       99.99
                            Laos |          4        0.01      100.00
---------------------------------+-----------------------------------
                           Total |     69,671      100.00

. *small countries not matched
. 
. keep if _merge==3
(69697 observations deleted)

. drop abbrev _merge majpow

. 
. 
. foreach x of varlist mid fmid war region lngdppc lnlifedeer lncap lnmilper lnmilex lnirst lnenergy lnupop lntpop
>  lntottr lnlifepen lnacli {
  2. rename `x' `x'1
  3. }

. 
. rename ccode ccode1

. rename ccode2 ccode

. sort ccode year

. 
. merge m:1 ccode year using "outdatatotal12111712.dta"
ccode was int now double

    Result                           # of obs.
    -----------------------------------------
    not matched                        40,018
        from master                    39,966  (_merge==1)
        from using                         52  (_merge==2)

    matched                           436,541  (_merge==3)
    -----------------------------------------

. 
. keep if _merge==3
(40018 observations deleted)

. drop abbrev _merge majpow

. 
. rename ccode ccode2

. 
. foreach x of varlist mid fmid war region lngdppc lnlifedeer lncap lnmilper lnmilex lnirst lnenergy lnupop lntpop
>  lntottr lnlifepen lnacli {
  2. rename `x' `x'2
  3. }

. 
. gen lnlifedeerl=min(lnlifedeer1, lnlifedeer2) if lnlifedeer1~=. & lnlifedeer2~=.

. gen lngdppcl=min(lngdppc1, lngdppc2) if lngdppc1~=. & lngdppc2~=.

. gen lncapl=min(lncap1, lncap2) if lncap1~=. & lncap2~=.

. gen lnmilperl=min(lnmilper1, lnmilper2) if lnmilper1~=. & lnmilper2~=.

. gen lnmilexl=min(lnmilex1, lnmilex2) if lnmilex1~=. & lnmilex2~=.

. gen lnirstl=min(lnirst1, lnirst2) if lnirst1~=. & lnirst2~=.

. gen lnenergyl=min(lnenergy1, lnenergy2) if lnenergy1~=. & lnenergy2~=.

. gen lnupopl=min(lnupop1, lnupop2) if lnupop1~=. & lnupop2~=.

. gen lntpopl=min(lntpop1, lntpop2) if lntpop1~=. & lntpop2~=.

. gen lntottrl=min(lntottr1, lntottr2) if lntottr1~=. & lntottr2~=.

. gen lnlifepenl=min(lnlifepen1, lnlifepen2) if lnlifepen1~=. & lnlifepen2~=.

. gen lnaclil=min(lnacli1, lnacli2) if lnacli1~=. & lnacli2~=.

. 
. gen lnlifedeerh=max(lnlifedeer1, lnlifedeer2) if lnlifedeer1~=. & lnlifedeer2~=.

. gen lngdppch=max(lngdppc1, lngdppc2) if lngdppc1~=. & lngdppc2~=.

. gen lncaph=max(lncap1, lncap2) if lncap1~=. & lncap2~=.

. gen lnmilperh=max(lnmilper1, lnmilper2) if lnmilper1~=. & lnmilper2~=.

. gen lnmilexh=max(lnmilex1, lnmilex2) if lnmilex1~=. & lnmilex2~=.

. gen lnirsth=max(lnirst1, lnirst2) if lnirst1~=. & lnirst2~=.

. gen lnenergyh=max(lnenergy1, lnenergy2) if lnenergy1~=. & lnenergy2~=.

. gen lnupoph=max(lnupop1, lnupop2) if lnupop1~=. & lnupop2~=.

. gen lntpoph=max(lntpop1, lntpop2) if lntpop1~=. & lntpop2~=.

. gen lntottrh=max(lntottr1, lntottr2) if lntottr1~=. & lntottr2~=.

. gen lnlifepenh=max(lnlifepen1, lnlifepen2) if lnlifepen1~=. & lnlifepen2~=.

. gen lnaclih=max(lnacli1, lnacli2) if lnacli1~=. & lnacli2~=.

. 
. *simplify dataset:
. keep country_name1 country_name2 ccode1 ccode2 year abbrev1 abbrev2 cwmid cwongo cwfatald dyadid lncprt mjpw dml
>  dmh contigl lndist polity22 contig distance numstate numGPs cwongonm cwmidnm cwpceyrs midonsl midongl fmidonsl 
> fmidongl warl midyears midyears2 midyears3 fmidyears fmidyears2 fmidyears3 waryears waryears2 waryears3 tpop MMC
> IE lnlifedeerl lngdppcl lnlifepenl lnlifedeerh lngdppch lnlifepenh

. 
. count
436541

. 
. gen m=12

. 
. sort ccode1 ccode2 year

. save "mi_12.dta", replace
(note: file mi_12.dta not found)
file mi_12.dta saved

. 
. 
. *imputation 13
. clear

. use "12-05-28_dem_CIE_analysis.dta"

. 
. drop if year<1950
(0 observations deleted)

. drop if year>2001
(0 observations deleted)

. 
. rename ccode1 ccode

. sort ccode year

. merge m:1 ccode year using "outdatatotal12111713.dta"
ccode was int now double
year was int now double
numstate was int now long
numGPs was byte now long
polity2 was byte now long

    Result                           # of obs.
    -----------------------------------------
    not matched                        69,697
        from master                    69,671  (_merge==1)
        from using                         26  (_merge==2)

    matched                           476,507  (_merge==3)
    -----------------------------------------

. 
. tab country_name1 if _merge==1, sort

       COW CCode Num for state 1 |      Freq.     Percent        Cum.
---------------------------------+-----------------------------------
                      Luxembourg |      5,560        7.98        7.98
                        Barbados |      5,485        7.87       15.85
                         Bahamas |      4,776        6.86       22.71
                         Grenada |      4,418        6.34       29.05
                         Iceland |      4,173        5.99       35.04
                           Malta |      4,165        5.98       41.02
                        Dominica |      3,886        5.58       46.59
                        Suriname |      3,881        5.57       52.17
                     Saint Lucia |      3,696        5.30       57.47
Saint Vincent and the Grenadines |      3,673        5.27       62.74
             Antigua and Barbuda |      3,366        4.83       67.57
                          Belize |      3,305        4.74       72.32
           Saint Kitts and Nevis |      3,057        4.39       76.70
                      Cape Verde |      2,678        3.84       80.55
           Sao Tome and Principe |      2,651        3.81       84.35
                   Liechtenstein |      1,703        2.44       86.80
                      Seychelles |      1,423        2.04       88.84
                      San Marino |      1,327        1.90       90.75
                          Monaco |      1,315        1.89       92.63
                         Andorra |      1,279        1.84       94.47
          Bosnia and Herzegovina |        893        1.28       95.75
                        Maldives |        607        0.87       96.62
                         Lebanon |        578        0.83       97.45
      German Democratic Republic |        232        0.33       97.78
                        Zimbabwe |        230        0.33       98.11
                          Brunei |        203        0.29       98.41
                            Peru |        162        0.23       98.64
                        Cambodia |        119        0.17       98.81
                           Ghana |        113        0.16       98.97
                         Vanuatu |        104        0.15       99.12
                         Tunisia |        101        0.14       99.27
                        Zanzibar |         90        0.13       99.39
                          Kuwait |         79        0.11       99.51
                          Uganda |         70        0.10       99.61
                         Hungary |         51        0.07       99.68
                     Afghanistan |         42        0.06       99.74
                Marshall Islands |         30        0.04       99.78
                           Syria |         27        0.04       99.82
                        Kiribati |         23        0.03       99.86
                      Bangladesh |         16        0.02       99.88
                           Palau |         16        0.02       99.90
                           Tonga |         15        0.02       99.92
                          Tuvalu |         14        0.02       99.94
                           Nauru |         12        0.02       99.96
             Republic of Vietnam |         12        0.02       99.98
                      Micronesia |         11        0.02       99.99
                            Laos |          4        0.01      100.00
---------------------------------+-----------------------------------
                           Total |     69,671      100.00

. *small countries not matched
. 
. keep if _merge==3
(69697 observations deleted)

. drop abbrev _merge majpow

. 
. 
. foreach x of varlist mid fmid war region lngdppc lnlifedeer lncap lnmilper lnmilex lnirst lnenergy lnupop lntpop
>  lntottr lnlifepen lnacli {
  2. rename `x' `x'1
  3. }

. 
. rename ccode ccode1

. rename ccode2 ccode

. sort ccode year

. 
. merge m:1 ccode year using "outdatatotal12111713.dta"
ccode was int now double

    Result                           # of obs.
    -----------------------------------------
    not matched                        40,018
        from master                    39,966  (_merge==1)
        from using                         52  (_merge==2)

    matched                           436,541  (_merge==3)
    -----------------------------------------

. 
. keep if _merge==3
(40018 observations deleted)

. drop abbrev _merge majpow

. 
. rename ccode ccode2

. 
. foreach x of varlist mid fmid war region lngdppc lnlifedeer lncap lnmilper lnmilex lnirst lnenergy lnupop lntpop
>  lntottr lnlifepen lnacli {
  2. rename `x' `x'2
  3. }

. 
. gen lnlifedeerl=min(lnlifedeer1, lnlifedeer2) if lnlifedeer1~=. & lnlifedeer2~=.

. gen lngdppcl=min(lngdppc1, lngdppc2) if lngdppc1~=. & lngdppc2~=.

. gen lncapl=min(lncap1, lncap2) if lncap1~=. & lncap2~=.

. gen lnmilperl=min(lnmilper1, lnmilper2) if lnmilper1~=. & lnmilper2~=.

. gen lnmilexl=min(lnmilex1, lnmilex2) if lnmilex1~=. & lnmilex2~=.

. gen lnirstl=min(lnirst1, lnirst2) if lnirst1~=. & lnirst2~=.

. gen lnenergyl=min(lnenergy1, lnenergy2) if lnenergy1~=. & lnenergy2~=.

. gen lnupopl=min(lnupop1, lnupop2) if lnupop1~=. & lnupop2~=.

. gen lntpopl=min(lntpop1, lntpop2) if lntpop1~=. & lntpop2~=.

. gen lntottrl=min(lntottr1, lntottr2) if lntottr1~=. & lntottr2~=.

. gen lnlifepenl=min(lnlifepen1, lnlifepen2) if lnlifepen1~=. & lnlifepen2~=.

. gen lnaclil=min(lnacli1, lnacli2) if lnacli1~=. & lnacli2~=.

. 
. gen lnlifedeerh=max(lnlifedeer1, lnlifedeer2) if lnlifedeer1~=. & lnlifedeer2~=.

. gen lngdppch=max(lngdppc1, lngdppc2) if lngdppc1~=. & lngdppc2~=.

. gen lncaph=max(lncap1, lncap2) if lncap1~=. & lncap2~=.

. gen lnmilperh=max(lnmilper1, lnmilper2) if lnmilper1~=. & lnmilper2~=.

. gen lnmilexh=max(lnmilex1, lnmilex2) if lnmilex1~=. & lnmilex2~=.

. gen lnirsth=max(lnirst1, lnirst2) if lnirst1~=. & lnirst2~=.

. gen lnenergyh=max(lnenergy1, lnenergy2) if lnenergy1~=. & lnenergy2~=.

. gen lnupoph=max(lnupop1, lnupop2) if lnupop1~=. & lnupop2~=.

. gen lntpoph=max(lntpop1, lntpop2) if lntpop1~=. & lntpop2~=.

. gen lntottrh=max(lntottr1, lntottr2) if lntottr1~=. & lntottr2~=.

. gen lnlifepenh=max(lnlifepen1, lnlifepen2) if lnlifepen1~=. & lnlifepen2~=.

. gen lnaclih=max(lnacli1, lnacli2) if lnacli1~=. & lnacli2~=.

. 
. *simplify dataset:
. keep country_name1 country_name2 ccode1 ccode2 year abbrev1 abbrev2 cwmid cwongo cwfatald dyadid lncprt mjpw dml
>  dmh contigl lndist polity22 contig distance numstate numGPs cwongonm cwmidnm cwpceyrs midonsl midongl fmidonsl 
> fmidongl warl midyears midyears2 midyears3 fmidyears fmidyears2 fmidyears3 waryears waryears2 waryears3 tpop MMC
> IE lnlifedeerl lngdppcl lnlifepenl lnlifedeerh lngdppch lnlifepenh

. 
. count
436541

. 
. gen m=13

. 
. sort ccode1 ccode2 year

. save "mi_13.dta", replace
(note: file mi_13.dta not found)
file mi_13.dta saved

. 
. 
. 
. 
. 
. 
. *imputation 4
. clear

. use "12-05-28_dem_CIE_analysis.dta"

. 
. drop if year<1950
(0 observations deleted)

. drop if year>2001
(0 observations deleted)

. 
. rename ccode1 ccode

. sort ccode year

. merge m:1 ccode year using "outdatatotal12111714.dta"
ccode was int now double
year was int now double
numstate was int now long
numGPs was byte now long
polity2 was byte now long

    Result                           # of obs.
    -----------------------------------------
    not matched                        69,697
        from master                    69,671  (_merge==1)
        from using                         26  (_merge==2)

    matched                           476,507  (_merge==3)
    -----------------------------------------

. 
. tab country_name1 if _merge==1, sort

       COW CCode Num for state 1 |      Freq.     Percent        Cum.
---------------------------------+-----------------------------------
                      Luxembourg |      5,560        7.98        7.98
                        Barbados |      5,485        7.87       15.85
                         Bahamas |      4,776        6.86       22.71
                         Grenada |      4,418        6.34       29.05
                         Iceland |      4,173        5.99       35.04
                           Malta |      4,165        5.98       41.02
                        Dominica |      3,886        5.58       46.59
                        Suriname |      3,881        5.57       52.17
                     Saint Lucia |      3,696        5.30       57.47
Saint Vincent and the Grenadines |      3,673        5.27       62.74
             Antigua and Barbuda |      3,366        4.83       67.57
                          Belize |      3,305        4.74       72.32
           Saint Kitts and Nevis |      3,057        4.39       76.70
                      Cape Verde |      2,678        3.84       80.55
           Sao Tome and Principe |      2,651        3.81       84.35
                   Liechtenstein |      1,703        2.44       86.80
                      Seychelles |      1,423        2.04       88.84
                      San Marino |      1,327        1.90       90.75
                          Monaco |      1,315        1.89       92.63
                         Andorra |      1,279        1.84       94.47
          Bosnia and Herzegovina |        893        1.28       95.75
                        Maldives |        607        0.87       96.62
                         Lebanon |        578        0.83       97.45
      German Democratic Republic |        232        0.33       97.78
                        Zimbabwe |        230        0.33       98.11
                          Brunei |        203        0.29       98.41
                            Peru |        162        0.23       98.64
                        Cambodia |        119        0.17       98.81
                           Ghana |        113        0.16       98.97
                         Vanuatu |        104        0.15       99.12
                         Tunisia |        101        0.14       99.27
                        Zanzibar |         90        0.13       99.39
                          Kuwait |         79        0.11       99.51
                          Uganda |         70        0.10       99.61
                         Hungary |         51        0.07       99.68
                     Afghanistan |         42        0.06       99.74
                Marshall Islands |         30        0.04       99.78
                           Syria |         27        0.04       99.82
                        Kiribati |         23        0.03       99.86
                      Bangladesh |         16        0.02       99.88
                           Palau |         16        0.02       99.90
                           Tonga |         15        0.02       99.92
                          Tuvalu |         14        0.02       99.94
                           Nauru |         12        0.02       99.96
             Republic of Vietnam |         12        0.02       99.98
                      Micronesia |         11        0.02       99.99
                            Laos |          4        0.01      100.00
---------------------------------+-----------------------------------
                           Total |     69,671      100.00

. *small countries not matched
. 
. keep if _merge==3
(69697 observations deleted)

. drop abbrev _merge majpow

. 
. 
. foreach x of varlist mid fmid war region lngdppc lnlifedeer lncap lnmilper lnmilex lnirst lnenergy lnupop lntpop
>  lntottr lnlifepen lnacli {
  2. rename `x' `x'1
  3. }

. 
. rename ccode ccode1

. rename ccode2 ccode

. sort ccode year

. 
. merge m:1 ccode year using "outdatatotal12111714.dta"
ccode was int now double

    Result                           # of obs.
    -----------------------------------------
    not matched                        40,018
        from master                    39,966  (_merge==1)
        from using                         52  (_merge==2)

    matched                           436,541  (_merge==3)
    -----------------------------------------

. 
. keep if _merge==3
(40018 observations deleted)

. drop abbrev _merge majpow

. 
. rename ccode ccode2

. 
. foreach x of varlist mid fmid war region lngdppc lnlifedeer lncap lnmilper lnmilex lnirst lnenergy lnupop lntpop
>  lntottr lnlifepen lnacli {
  2. rename `x' `x'2
  3. }

. 
. gen lnlifedeerl=min(lnlifedeer1, lnlifedeer2) if lnlifedeer1~=. & lnlifedeer2~=.

. gen lngdppcl=min(lngdppc1, lngdppc2) if lngdppc1~=. & lngdppc2~=.

. gen lncapl=min(lncap1, lncap2) if lncap1~=. & lncap2~=.

. gen lnmilperl=min(lnmilper1, lnmilper2) if lnmilper1~=. & lnmilper2~=.

. gen lnmilexl=min(lnmilex1, lnmilex2) if lnmilex1~=. & lnmilex2~=.

. gen lnirstl=min(lnirst1, lnirst2) if lnirst1~=. & lnirst2~=.

. gen lnenergyl=min(lnenergy1, lnenergy2) if lnenergy1~=. & lnenergy2~=.

. gen lnupopl=min(lnupop1, lnupop2) if lnupop1~=. & lnupop2~=.

. gen lntpopl=min(lntpop1, lntpop2) if lntpop1~=. & lntpop2~=.

. gen lntottrl=min(lntottr1, lntottr2) if lntottr1~=. & lntottr2~=.

. gen lnlifepenl=min(lnlifepen1, lnlifepen2) if lnlifepen1~=. & lnlifepen2~=.

. gen lnaclil=min(lnacli1, lnacli2) if lnacli1~=. & lnacli2~=.

. 
. gen lnlifedeerh=max(lnlifedeer1, lnlifedeer2) if lnlifedeer1~=. & lnlifedeer2~=.

. gen lngdppch=max(lngdppc1, lngdppc2) if lngdppc1~=. & lngdppc2~=.

. gen lncaph=max(lncap1, lncap2) if lncap1~=. & lncap2~=.

. gen lnmilperh=max(lnmilper1, lnmilper2) if lnmilper1~=. & lnmilper2~=.

. gen lnmilexh=max(lnmilex1, lnmilex2) if lnmilex1~=. & lnmilex2~=.

. gen lnirsth=max(lnirst1, lnirst2) if lnirst1~=. & lnirst2~=.

. gen lnenergyh=max(lnenergy1, lnenergy2) if lnenergy1~=. & lnenergy2~=.

. gen lnupoph=max(lnupop1, lnupop2) if lnupop1~=. & lnupop2~=.

. gen lntpoph=max(lntpop1, lntpop2) if lntpop1~=. & lntpop2~=.

. gen lntottrh=max(lntottr1, lntottr2) if lntottr1~=. & lntottr2~=.

. gen lnlifepenh=max(lnlifepen1, lnlifepen2) if lnlifepen1~=. & lnlifepen2~=.

. gen lnaclih=max(lnacli1, lnacli2) if lnacli1~=. & lnacli2~=.

. 
. *simplify dataset:
. keep country_name1 country_name2 ccode1 ccode2 year abbrev1 abbrev2 cwmid cwongo cwfatald dyadid lncprt mjpw dml
>  dmh contigl lndist polity22 contig distance numstate numGPs cwongonm cwmidnm cwpceyrs midonsl midongl fmidonsl 
> fmidongl warl midyears midyears2 midyears3 fmidyears fmidyears2 fmidyears3 waryears waryears2 waryears3 tpop MMC
> IE lnlifedeerl lngdppcl lnlifepenl lnlifedeerh lngdppch lnlifepenh

. 
. count
436541

. 
. gen m=14

. 
. sort ccode1 ccode2 year

. save "mi_14.dta", replace
(note: file mi_14.dta not found)
file mi_14.dta saved

. 
. 
. 
. *imputation 15
. clear

. use "12-05-28_dem_CIE_analysis.dta"

. 
. drop if year<1950
(0 observations deleted)

. drop if year>2001
(0 observations deleted)

. 
. rename ccode1 ccode

. sort ccode year

. merge m:1 ccode year using "outdatatotal12111715.dta"
ccode was int now double
year was int now double
numstate was int now long
numGPs was byte now long
polity2 was byte now long

    Result                           # of obs.
    -----------------------------------------
    not matched                        69,697
        from master                    69,671  (_merge==1)
        from using                         26  (_merge==2)

    matched                           476,507  (_merge==3)
    -----------------------------------------

. 
. tab country_name1 if _merge==1, sort

       COW CCode Num for state 1 |      Freq.     Percent        Cum.
---------------------------------+-----------------------------------
                      Luxembourg |      5,560        7.98        7.98
                        Barbados |      5,485        7.87       15.85
                         Bahamas |      4,776        6.86       22.71
                         Grenada |      4,418        6.34       29.05
                         Iceland |      4,173        5.99       35.04
                           Malta |      4,165        5.98       41.02
                        Dominica |      3,886        5.58       46.59
                        Suriname |      3,881        5.57       52.17
                     Saint Lucia |      3,696        5.30       57.47
Saint Vincent and the Grenadines |      3,673        5.27       62.74
             Antigua and Barbuda |      3,366        4.83       67.57
                          Belize |      3,305        4.74       72.32
           Saint Kitts and Nevis |      3,057        4.39       76.70
                      Cape Verde |      2,678        3.84       80.55
           Sao Tome and Principe |      2,651        3.81       84.35
                   Liechtenstein |      1,703        2.44       86.80
                      Seychelles |      1,423        2.04       88.84
                      San Marino |      1,327        1.90       90.75
                          Monaco |      1,315        1.89       92.63
                         Andorra |      1,279        1.84       94.47
          Bosnia and Herzegovina |        893        1.28       95.75
                        Maldives |        607        0.87       96.62
                         Lebanon |        578        0.83       97.45
      German Democratic Republic |        232        0.33       97.78
                        Zimbabwe |        230        0.33       98.11
                          Brunei |        203        0.29       98.41
                            Peru |        162        0.23       98.64
                        Cambodia |        119        0.17       98.81
                           Ghana |        113        0.16       98.97
                         Vanuatu |        104        0.15       99.12
                         Tunisia |        101        0.14       99.27
                        Zanzibar |         90        0.13       99.39
                          Kuwait |         79        0.11       99.51
                          Uganda |         70        0.10       99.61
                         Hungary |         51        0.07       99.68
                     Afghanistan |         42        0.06       99.74
                Marshall Islands |         30        0.04       99.78
                           Syria |         27        0.04       99.82
                        Kiribati |         23        0.03       99.86
                      Bangladesh |         16        0.02       99.88
                           Palau |         16        0.02       99.90
                           Tonga |         15        0.02       99.92
                          Tuvalu |         14        0.02       99.94
                           Nauru |         12        0.02       99.96
             Republic of Vietnam |         12        0.02       99.98
                      Micronesia |         11        0.02       99.99
                            Laos |          4        0.01      100.00
---------------------------------+-----------------------------------
                           Total |     69,671      100.00

. *small countries not matched
. 
. keep if _merge==3
(69697 observations deleted)

. drop abbrev _merge majpow

. 
. 
. foreach x of varlist mid fmid war region lngdppc lnlifedeer lncap lnmilper lnmilex lnirst lnenergy lnupop lntpop
>  lntottr lnlifepen lnacli {
  2. rename `x' `x'1
  3. }

. 
. rename ccode ccode1

. rename ccode2 ccode

. sort ccode year

. 
. merge m:1 ccode year using "outdatatotal12111715.dta"
ccode was int now double

    Result                           # of obs.
    -----------------------------------------
    not matched                        40,018
        from master                    39,966  (_merge==1)
        from using                         52  (_merge==2)

    matched                           436,541  (_merge==3)
    -----------------------------------------

. 
. keep if _merge==3
(40018 observations deleted)

. drop abbrev _merge majpow

. 
. rename ccode ccode2

. 
. foreach x of varlist mid fmid war region lngdppc lnlifedeer lncap lnmilper lnmilex lnirst lnenergy lnupop lntpop
>  lntottr lnlifepen lnacli {
  2. rename `x' `x'2
  3. }

. 
. gen lnlifedeerl=min(lnlifedeer1, lnlifedeer2) if lnlifedeer1~=. & lnlifedeer2~=.

. gen lngdppcl=min(lngdppc1, lngdppc2) if lngdppc1~=. & lngdppc2~=.

. gen lncapl=min(lncap1, lncap2) if lncap1~=. & lncap2~=.

. gen lnmilperl=min(lnmilper1, lnmilper2) if lnmilper1~=. & lnmilper2~=.

. gen lnmilexl=min(lnmilex1, lnmilex2) if lnmilex1~=. & lnmilex2~=.

. gen lnirstl=min(lnirst1, lnirst2) if lnirst1~=. & lnirst2~=.

. gen lnenergyl=min(lnenergy1, lnenergy2) if lnenergy1~=. & lnenergy2~=.

. gen lnupopl=min(lnupop1, lnupop2) if lnupop1~=. & lnupop2~=.

. gen lntpopl=min(lntpop1, lntpop2) if lntpop1~=. & lntpop2~=.

. gen lntottrl=min(lntottr1, lntottr2) if lntottr1~=. & lntottr2~=.

. gen lnlifepenl=min(lnlifepen1, lnlifepen2) if lnlifepen1~=. & lnlifepen2~=.

. gen lnaclil=min(lnacli1, lnacli2) if lnacli1~=. & lnacli2~=.

. 
. gen lnlifedeerh=max(lnlifedeer1, lnlifedeer2) if lnlifedeer1~=. & lnlifedeer2~=.

. gen lngdppch=max(lngdppc1, lngdppc2) if lngdppc1~=. & lngdppc2~=.

. gen lncaph=max(lncap1, lncap2) if lncap1~=. & lncap2~=.

. gen lnmilperh=max(lnmilper1, lnmilper2) if lnmilper1~=. & lnmilper2~=.

. gen lnmilexh=max(lnmilex1, lnmilex2) if lnmilex1~=. & lnmilex2~=.

. gen lnirsth=max(lnirst1, lnirst2) if lnirst1~=. & lnirst2~=.

. gen lnenergyh=max(lnenergy1, lnenergy2) if lnenergy1~=. & lnenergy2~=.

. gen lnupoph=max(lnupop1, lnupop2) if lnupop1~=. & lnupop2~=.

. gen lntpoph=max(lntpop1, lntpop2) if lntpop1~=. & lntpop2~=.

. gen lntottrh=max(lntottr1, lntottr2) if lntottr1~=. & lntottr2~=.

. gen lnlifepenh=max(lnlifepen1, lnlifepen2) if lnlifepen1~=. & lnlifepen2~=.

. gen lnaclih=max(lnacli1, lnacli2) if lnacli1~=. & lnacli2~=.

. 
. *simplify dataset:
. keep country_name1 country_name2 ccode1 ccode2 year abbrev1 abbrev2 cwmid cwongo cwfatald dyadid lncprt mjpw dml
>  dmh contigl lndist polity22 contig distance numstate numGPs cwongonm cwmidnm cwpceyrs midonsl midongl fmidonsl 
> fmidongl warl midyears midyears2 midyears3 fmidyears fmidyears2 fmidyears3 waryears waryears2 waryears3 tpop MMC
> IE lnlifedeerl lngdppcl lnlifepenl lnlifedeerh lngdppch lnlifepenh

. 
. count
436541

. 
. gen m=15

. 
. sort ccode1 ccode2 year

. save "mi_15.dta", replace
(note: file mi_15.dta not found)
file mi_15.dta saved

. 
. 
. 
. 
. *imputation 6
. clear

. use "12-05-28_dem_CIE_analysis.dta"

. 
. drop if year<1950
(0 observations deleted)

. drop if year>2001
(0 observations deleted)

. 
. rename ccode1 ccode

. sort ccode year

. merge m:1 ccode year using "outdatatotal12111716.dta"
ccode was int now double
year was int now double
numstate was int now long
numGPs was byte now long
polity2 was byte now long

    Result                           # of obs.
    -----------------------------------------
    not matched                        69,697
        from master                    69,671  (_merge==1)
        from using                         26  (_merge==2)

    matched                           476,507  (_merge==3)
    -----------------------------------------

. 
. tab country_name1 if _merge==1, sort

       COW CCode Num for state 1 |      Freq.     Percent        Cum.
---------------------------------+-----------------------------------
                      Luxembourg |      5,560        7.98        7.98
                        Barbados |      5,485        7.87       15.85
                         Bahamas |      4,776        6.86       22.71
                         Grenada |      4,418        6.34       29.05
                         Iceland |      4,173        5.99       35.04
                           Malta |      4,165        5.98       41.02
                        Dominica |      3,886        5.58       46.59
                        Suriname |      3,881        5.57       52.17
                     Saint Lucia |      3,696        5.30       57.47
Saint Vincent and the Grenadines |      3,673        5.27       62.74
             Antigua and Barbuda |      3,366        4.83       67.57
                          Belize |      3,305        4.74       72.32
           Saint Kitts and Nevis |      3,057        4.39       76.70
                      Cape Verde |      2,678        3.84       80.55
           Sao Tome and Principe |      2,651        3.81       84.35
                   Liechtenstein |      1,703        2.44       86.80
                      Seychelles |      1,423        2.04       88.84
                      San Marino |      1,327        1.90       90.75
                          Monaco |      1,315        1.89       92.63
                         Andorra |      1,279        1.84       94.47
          Bosnia and Herzegovina |        893        1.28       95.75
                        Maldives |        607        0.87       96.62
                         Lebanon |        578        0.83       97.45
      German Democratic Republic |        232        0.33       97.78
                        Zimbabwe |        230        0.33       98.11
                          Brunei |        203        0.29       98.41
                            Peru |        162        0.23       98.64
                        Cambodia |        119        0.17       98.81
                           Ghana |        113        0.16       98.97
                         Vanuatu |        104        0.15       99.12
                         Tunisia |        101        0.14       99.27
                        Zanzibar |         90        0.13       99.39
                          Kuwait |         79        0.11       99.51
                          Uganda |         70        0.10       99.61
                         Hungary |         51        0.07       99.68
                     Afghanistan |         42        0.06       99.74
                Marshall Islands |         30        0.04       99.78
                           Syria |         27        0.04       99.82
                        Kiribati |         23        0.03       99.86
                      Bangladesh |         16        0.02       99.88
                           Palau |         16        0.02       99.90
                           Tonga |         15        0.02       99.92
                          Tuvalu |         14        0.02       99.94
                           Nauru |         12        0.02       99.96
             Republic of Vietnam |         12        0.02       99.98
                      Micronesia |         11        0.02       99.99
                            Laos |          4        0.01      100.00
---------------------------------+-----------------------------------
                           Total |     69,671      100.00

. *small countries not matched
. 
. keep if _merge==3
(69697 observations deleted)

. drop abbrev _merge majpow

. 
. 
. foreach x of varlist mid fmid war region lngdppc lnlifedeer lncap lnmilper lnmilex lnirst lnenergy lnupop lntpop
>  lntottr lnlifepen lnacli {
  2. rename `x' `x'1
  3. }

. 
. rename ccode ccode1

. rename ccode2 ccode

. sort ccode year

. 
. merge m:1 ccode year using "outdatatotal12111716.dta"
ccode was int now double

    Result                           # of obs.
    -----------------------------------------
    not matched                        40,018
        from master                    39,966  (_merge==1)
        from using                         52  (_merge==2)

    matched                           436,541  (_merge==3)
    -----------------------------------------

. 
. keep if _merge==3
(40018 observations deleted)

. drop abbrev _merge majpow

. 
. rename ccode ccode2

. 
. foreach x of varlist mid fmid war region lngdppc lnlifedeer lncap lnmilper lnmilex lnirst lnenergy lnupop lntpop
>  lntottr lnlifepen lnacli {
  2. rename `x' `x'2
  3. }

. 
. gen lnlifedeerl=min(lnlifedeer1, lnlifedeer2) if lnlifedeer1~=. & lnlifedeer2~=.

. gen lngdppcl=min(lngdppc1, lngdppc2) if lngdppc1~=. & lngdppc2~=.

. gen lncapl=min(lncap1, lncap2) if lncap1~=. & lncap2~=.

. gen lnmilperl=min(lnmilper1, lnmilper2) if lnmilper1~=. & lnmilper2~=.

. gen lnmilexl=min(lnmilex1, lnmilex2) if lnmilex1~=. & lnmilex2~=.

. gen lnirstl=min(lnirst1, lnirst2) if lnirst1~=. & lnirst2~=.

. gen lnenergyl=min(lnenergy1, lnenergy2) if lnenergy1~=. & lnenergy2~=.

. gen lnupopl=min(lnupop1, lnupop2) if lnupop1~=. & lnupop2~=.

. gen lntpopl=min(lntpop1, lntpop2) if lntpop1~=. & lntpop2~=.

. gen lntottrl=min(lntottr1, lntottr2) if lntottr1~=. & lntottr2~=.

. gen lnlifepenl=min(lnlifepen1, lnlifepen2) if lnlifepen1~=. & lnlifepen2~=.

. gen lnaclil=min(lnacli1, lnacli2) if lnacli1~=. & lnacli2~=.

. 
. gen lnlifedeerh=max(lnlifedeer1, lnlifedeer2) if lnlifedeer1~=. & lnlifedeer2~=.

. gen lngdppch=max(lngdppc1, lngdppc2) if lngdppc1~=. & lngdppc2~=.

. gen lncaph=max(lncap1, lncap2) if lncap1~=. & lncap2~=.

. gen lnmilperh=max(lnmilper1, lnmilper2) if lnmilper1~=. & lnmilper2~=.

. gen lnmilexh=max(lnmilex1, lnmilex2) if lnmilex1~=. & lnmilex2~=.

. gen lnirsth=max(lnirst1, lnirst2) if lnirst1~=. & lnirst2~=.

. gen lnenergyh=max(lnenergy1, lnenergy2) if lnenergy1~=. & lnenergy2~=.

. gen lnupoph=max(lnupop1, lnupop2) if lnupop1~=. & lnupop2~=.

. gen lntpoph=max(lntpop1, lntpop2) if lntpop1~=. & lntpop2~=.

. gen lntottrh=max(lntottr1, lntottr2) if lntottr1~=. & lntottr2~=.

. gen lnlifepenh=max(lnlifepen1, lnlifepen2) if lnlifepen1~=. & lnlifepen2~=.

. gen lnaclih=max(lnacli1, lnacli2) if lnacli1~=. & lnacli2~=.

. 
. *simplify dataset:
. keep country_name1 country_name2 ccode1 ccode2 year abbrev1 abbrev2 cwmid cwongo cwfatald dyadid lncprt mjpw dml
>  dmh contigl lndist polity22 contig distance numstate numGPs cwongonm cwmidnm cwpceyrs midonsl midongl fmidonsl 
> fmidongl warl midyears midyears2 midyears3 fmidyears fmidyears2 fmidyears3 waryears waryears2 waryears3 tpop MMC
> IE lnlifedeerl lngdppcl lnlifepenl lnlifedeerh lngdppch lnlifepenh

. 
. count
436541

. 
. gen m=16

. 
. sort ccode1 ccode2 year

. save "mi_16.dta", replace
(note: file mi_16.dta not found)
file mi_16.dta saved

. 
. 
. 
. 
. *imputation 17
. clear

. use "12-05-28_dem_CIE_analysis.dta"

. 
. drop if year<1950
(0 observations deleted)

. drop if year>2001
(0 observations deleted)

. 
. rename ccode1 ccode

. sort ccode year

. merge m:1 ccode year using "outdatatotal12111717.dta"
ccode was int now double
year was int now double
numstate was int now long
numGPs was byte now long
polity2 was byte now long

    Result                           # of obs.
    -----------------------------------------
    not matched                        69,697
        from master                    69,671  (_merge==1)
        from using                         26  (_merge==2)

    matched                           476,507  (_merge==3)
    -----------------------------------------

. 
. tab country_name1 if _merge==1, sort

       COW CCode Num for state 1 |      Freq.     Percent        Cum.
---------------------------------+-----------------------------------
                      Luxembourg |      5,560        7.98        7.98
                        Barbados |      5,485        7.87       15.85
                         Bahamas |      4,776        6.86       22.71
                         Grenada |      4,418        6.34       29.05
                         Iceland |      4,173        5.99       35.04
                           Malta |      4,165        5.98       41.02
                        Dominica |      3,886        5.58       46.59
                        Suriname |      3,881        5.57       52.17
                     Saint Lucia |      3,696        5.30       57.47
Saint Vincent and the Grenadines |      3,673        5.27       62.74
             Antigua and Barbuda |      3,366        4.83       67.57
                          Belize |      3,305        4.74       72.32
           Saint Kitts and Nevis |      3,057        4.39       76.70
                      Cape Verde |      2,678        3.84       80.55
           Sao Tome and Principe |      2,651        3.81       84.35
                   Liechtenstein |      1,703        2.44       86.80
                      Seychelles |      1,423        2.04       88.84
                      San Marino |      1,327        1.90       90.75
                          Monaco |      1,315        1.89       92.63
                         Andorra |      1,279        1.84       94.47
          Bosnia and Herzegovina |        893        1.28       95.75
                        Maldives |        607        0.87       96.62
                         Lebanon |        578        0.83       97.45
      German Democratic Republic |        232        0.33       97.78
                        Zimbabwe |        230        0.33       98.11
                          Brunei |        203        0.29       98.41
                            Peru |        162        0.23       98.64
                        Cambodia |        119        0.17       98.81
                           Ghana |        113        0.16       98.97
                         Vanuatu |        104        0.15       99.12
                         Tunisia |        101        0.14       99.27
                        Zanzibar |         90        0.13       99.39
                          Kuwait |         79        0.11       99.51
                          Uganda |         70        0.10       99.61
                         Hungary |         51        0.07       99.68
                     Afghanistan |         42        0.06       99.74
                Marshall Islands |         30        0.04       99.78
                           Syria |         27        0.04       99.82
                        Kiribati |         23        0.03       99.86
                      Bangladesh |         16        0.02       99.88
                           Palau |         16        0.02       99.90
                           Tonga |         15        0.02       99.92
                          Tuvalu |         14        0.02       99.94
                           Nauru |         12        0.02       99.96
             Republic of Vietnam |         12        0.02       99.98
                      Micronesia |         11        0.02       99.99
                            Laos |          4        0.01      100.00
---------------------------------+-----------------------------------
                           Total |     69,671      100.00

. *small countries not matched
. 
. keep if _merge==3
(69697 observations deleted)

. drop abbrev _merge majpow

. 
. 
. foreach x of varlist mid fmid war region lngdppc lnlifedeer lncap lnmilper lnmilex lnirst lnenergy lnupop lntpop
>  lntottr lnlifepen lnacli {
  2. rename `x' `x'1
  3. }

. 
. rename ccode ccode1

. rename ccode2 ccode

. sort ccode year

. 
. merge m:1 ccode year using "outdatatotal12111717.dta"
ccode was int now double

    Result                           # of obs.
    -----------------------------------------
    not matched                        40,018
        from master                    39,966  (_merge==1)
        from using                         52  (_merge==2)

    matched                           436,541  (_merge==3)
    -----------------------------------------

. 
. keep if _merge==3
(40018 observations deleted)

. drop abbrev _merge majpow

. 
. rename ccode ccode2

. 
. foreach x of varlist mid fmid war region lngdppc lnlifedeer lncap lnmilper lnmilex lnirst lnenergy lnupop lntpop
>  lntottr lnlifepen lnacli {
  2. rename `x' `x'2
  3. }

. 
. gen lnlifedeerl=min(lnlifedeer1, lnlifedeer2) if lnlifedeer1~=. & lnlifedeer2~=.

. gen lngdppcl=min(lngdppc1, lngdppc2) if lngdppc1~=. & lngdppc2~=.

. gen lncapl=min(lncap1, lncap2) if lncap1~=. & lncap2~=.

. gen lnmilperl=min(lnmilper1, lnmilper2) if lnmilper1~=. & lnmilper2~=.

. gen lnmilexl=min(lnmilex1, lnmilex2) if lnmilex1~=. & lnmilex2~=.

. gen lnirstl=min(lnirst1, lnirst2) if lnirst1~=. & lnirst2~=.

. gen lnenergyl=min(lnenergy1, lnenergy2) if lnenergy1~=. & lnenergy2~=.

. gen lnupopl=min(lnupop1, lnupop2) if lnupop1~=. & lnupop2~=.

. gen lntpopl=min(lntpop1, lntpop2) if lntpop1~=. & lntpop2~=.

. gen lntottrl=min(lntottr1, lntottr2) if lntottr1~=. & lntottr2~=.

. gen lnlifepenl=min(lnlifepen1, lnlifepen2) if lnlifepen1~=. & lnlifepen2~=.

. gen lnaclil=min(lnacli1, lnacli2) if lnacli1~=. & lnacli2~=.

. 
. gen lnlifedeerh=max(lnlifedeer1, lnlifedeer2) if lnlifedeer1~=. & lnlifedeer2~=.

. gen lngdppch=max(lngdppc1, lngdppc2) if lngdppc1~=. & lngdppc2~=.

. gen lncaph=max(lncap1, lncap2) if lncap1~=. & lncap2~=.

. gen lnmilperh=max(lnmilper1, lnmilper2) if lnmilper1~=. & lnmilper2~=.

. gen lnmilexh=max(lnmilex1, lnmilex2) if lnmilex1~=. & lnmilex2~=.

. gen lnirsth=max(lnirst1, lnirst2) if lnirst1~=. & lnirst2~=.

. gen lnenergyh=max(lnenergy1, lnenergy2) if lnenergy1~=. & lnenergy2~=.

. gen lnupoph=max(lnupop1, lnupop2) if lnupop1~=. & lnupop2~=.

. gen lntpoph=max(lntpop1, lntpop2) if lntpop1~=. & lntpop2~=.

. gen lntottrh=max(lntottr1, lntottr2) if lntottr1~=. & lntottr2~=.

. gen lnlifepenh=max(lnlifepen1, lnlifepen2) if lnlifepen1~=. & lnlifepen2~=.

. gen lnaclih=max(lnacli1, lnacli2) if lnacli1~=. & lnacli2~=.

. 
. *simplify dataset:
. keep country_name1 country_name2 ccode1 ccode2 year abbrev1 abbrev2 cwmid cwongo cwfatald dyadid lncprt mjpw dml
>  dmh contigl lndist polity22 contig distance numstate numGPs cwongonm cwmidnm cwpceyrs midonsl midongl fmidonsl 
> fmidongl warl midyears midyears2 midyears3 fmidyears fmidyears2 fmidyears3 waryears waryears2 waryears3 tpop MMC
> IE lnlifedeerl lngdppcl lnlifepenl lnlifedeerh lngdppch lnlifepenh

. 
. count
436541

. 
. gen m=17

. 
. sort ccode1 ccode2 year

. save "mi_17.dta", replace
(note: file mi_17.dta not found)
file mi_17.dta saved

. 
. 
. 
. 
. *imputation 18
. clear

. use "12-05-28_dem_CIE_analysis.dta"

. 
. drop if year<1950
(0 observations deleted)

. drop if year>2001
(0 observations deleted)

. 
. rename ccode1 ccode

. sort ccode year

. merge m:1 ccode year using "outdatatotal12111718.dta"
ccode was int now double
year was int now double
numstate was int now long
numGPs was byte now long
polity2 was byte now long

    Result                           # of obs.
    -----------------------------------------
    not matched                        69,697
        from master                    69,671  (_merge==1)
        from using                         26  (_merge==2)

    matched                           476,507  (_merge==3)
    -----------------------------------------

. 
. tab country_name1 if _merge==1, sort

       COW CCode Num for state 1 |      Freq.     Percent        Cum.
---------------------------------+-----------------------------------
                      Luxembourg |      5,560        7.98        7.98
                        Barbados |      5,485        7.87       15.85
                         Bahamas |      4,776        6.86       22.71
                         Grenada |      4,418        6.34       29.05
                         Iceland |      4,173        5.99       35.04
                           Malta |      4,165        5.98       41.02
                        Dominica |      3,886        5.58       46.59
                        Suriname |      3,881        5.57       52.17
                     Saint Lucia |      3,696        5.30       57.47
Saint Vincent and the Grenadines |      3,673        5.27       62.74
             Antigua and Barbuda |      3,366        4.83       67.57
                          Belize |      3,305        4.74       72.32
           Saint Kitts and Nevis |      3,057        4.39       76.70
                      Cape Verde |      2,678        3.84       80.55
           Sao Tome and Principe |      2,651        3.81       84.35
                   Liechtenstein |      1,703        2.44       86.80
                      Seychelles |      1,423        2.04       88.84
                      San Marino |      1,327        1.90       90.75
                          Monaco |      1,315        1.89       92.63
                         Andorra |      1,279        1.84       94.47
          Bosnia and Herzegovina |        893        1.28       95.75
                        Maldives |        607        0.87       96.62
                         Lebanon |        578        0.83       97.45
      German Democratic Republic |        232        0.33       97.78
                        Zimbabwe |        230        0.33       98.11
                          Brunei |        203        0.29       98.41
                            Peru |        162        0.23       98.64
                        Cambodia |        119        0.17       98.81
                           Ghana |        113        0.16       98.97
                         Vanuatu |        104        0.15       99.12
                         Tunisia |        101        0.14       99.27
                        Zanzibar |         90        0.13       99.39
                          Kuwait |         79        0.11       99.51
                          Uganda |         70        0.10       99.61
                         Hungary |         51        0.07       99.68
                     Afghanistan |         42        0.06       99.74
                Marshall Islands |         30        0.04       99.78
                           Syria |         27        0.04       99.82
                        Kiribati |         23        0.03       99.86
                      Bangladesh |         16        0.02       99.88
                           Palau |         16        0.02       99.90
                           Tonga |         15        0.02       99.92
                          Tuvalu |         14        0.02       99.94
                           Nauru |         12        0.02       99.96
             Republic of Vietnam |         12        0.02       99.98
                      Micronesia |         11        0.02       99.99
                            Laos |          4        0.01      100.00
---------------------------------+-----------------------------------
                           Total |     69,671      100.00

. *small countries not matched
. 
. keep if _merge==3
(69697 observations deleted)

. drop abbrev _merge majpow

. 
. 
. foreach x of varlist mid fmid war region lngdppc lnlifedeer lncap lnmilper lnmilex lnirst lnenergy lnupop lntpop
>  lntottr lnlifepen lnacli {
  2. rename `x' `x'1
  3. }

. 
. rename ccode ccode1

. rename ccode2 ccode

. sort ccode year

. 
. merge m:1 ccode year using "outdatatotal12111718.dta"
ccode was int now double

    Result                           # of obs.
    -----------------------------------------
    not matched                        40,018
        from master                    39,966  (_merge==1)
        from using                         52  (_merge==2)

    matched                           436,541  (_merge==3)
    -----------------------------------------

. 
. keep if _merge==3
(40018 observations deleted)

. drop abbrev _merge majpow

. 
. rename ccode ccode2

. 
. foreach x of varlist mid fmid war region lngdppc lnlifedeer lncap lnmilper lnmilex lnirst lnenergy lnupop lntpop
>  lntottr lnlifepen lnacli {
  2. rename `x' `x'2
  3. }

. 
. gen lnlifedeerl=min(lnlifedeer1, lnlifedeer2) if lnlifedeer1~=. & lnlifedeer2~=.

. gen lngdppcl=min(lngdppc1, lngdppc2) if lngdppc1~=. & lngdppc2~=.

. gen lncapl=min(lncap1, lncap2) if lncap1~=. & lncap2~=.

. gen lnmilperl=min(lnmilper1, lnmilper2) if lnmilper1~=. & lnmilper2~=.

. gen lnmilexl=min(lnmilex1, lnmilex2) if lnmilex1~=. & lnmilex2~=.

. gen lnirstl=min(lnirst1, lnirst2) if lnirst1~=. & lnirst2~=.

. gen lnenergyl=min(lnenergy1, lnenergy2) if lnenergy1~=. & lnenergy2~=.

. gen lnupopl=min(lnupop1, lnupop2) if lnupop1~=. & lnupop2~=.

. gen lntpopl=min(lntpop1, lntpop2) if lntpop1~=. & lntpop2~=.

. gen lntottrl=min(lntottr1, lntottr2) if lntottr1~=. & lntottr2~=.

. gen lnlifepenl=min(lnlifepen1, lnlifepen2) if lnlifepen1~=. & lnlifepen2~=.

. gen lnaclil=min(lnacli1, lnacli2) if lnacli1~=. & lnacli2~=.

. 
. gen lnlifedeerh=max(lnlifedeer1, lnlifedeer2) if lnlifedeer1~=. & lnlifedeer2~=.

. gen lngdppch=max(lngdppc1, lngdppc2) if lngdppc1~=. & lngdppc2~=.

. gen lncaph=max(lncap1, lncap2) if lncap1~=. & lncap2~=.

. gen lnmilperh=max(lnmilper1, lnmilper2) if lnmilper1~=. & lnmilper2~=.

. gen lnmilexh=max(lnmilex1, lnmilex2) if lnmilex1~=. & lnmilex2~=.

. gen lnirsth=max(lnirst1, lnirst2) if lnirst1~=. & lnirst2~=.

. gen lnenergyh=max(lnenergy1, lnenergy2) if lnenergy1~=. & lnenergy2~=.

. gen lnupoph=max(lnupop1, lnupop2) if lnupop1~=. & lnupop2~=.

. gen lntpoph=max(lntpop1, lntpop2) if lntpop1~=. & lntpop2~=.

. gen lntottrh=max(lntottr1, lntottr2) if lntottr1~=. & lntottr2~=.

. gen lnlifepenh=max(lnlifepen1, lnlifepen2) if lnlifepen1~=. & lnlifepen2~=.

. gen lnaclih=max(lnacli1, lnacli2) if lnacli1~=. & lnacli2~=.

. 
. *simplify dataset:
. keep country_name1 country_name2 ccode1 ccode2 year abbrev1 abbrev2 cwmid cwongo cwfatald dyadid lncprt mjpw dml
>  dmh contigl lndist polity22 contig distance numstate numGPs cwongonm cwmidnm cwpceyrs midonsl midongl fmidonsl 
> fmidongl warl midyears midyears2 midyears3 fmidyears fmidyears2 fmidyears3 waryears waryears2 waryears3 tpop MMC
> IE lnlifedeerl lngdppcl lnlifepenl lnlifedeerh lngdppch lnlifepenh

. 
. count
436541

. 
. gen m=18

. 
. sort ccode1 ccode2 year

. save "mi_18.dta", replace
(note: file mi_18.dta not found)
file mi_18.dta saved

. 
. 
. 
. *imputation 19
. clear

. use "12-05-28_dem_CIE_analysis.dta"

. 
. drop if year<1950
(0 observations deleted)

. drop if year>2001
(0 observations deleted)

. 
. rename ccode1 ccode

. sort ccode year

. merge m:1 ccode year using "outdatatotal12111719.dta"
ccode was int now double
year was int now double
numstate was int now long
numGPs was byte now long
polity2 was byte now long

    Result                           # of obs.
    -----------------------------------------
    not matched                        69,697
        from master                    69,671  (_merge==1)
        from using                         26  (_merge==2)

    matched                           476,507  (_merge==3)
    -----------------------------------------

. 
. tab country_name1 if _merge==1, sort

       COW CCode Num for state 1 |      Freq.     Percent        Cum.
---------------------------------+-----------------------------------
                      Luxembourg |      5,560        7.98        7.98
                        Barbados |      5,485        7.87       15.85
                         Bahamas |      4,776        6.86       22.71
                         Grenada |      4,418        6.34       29.05
                         Iceland |      4,173        5.99       35.04
                           Malta |      4,165        5.98       41.02
                        Dominica |      3,886        5.58       46.59
                        Suriname |      3,881        5.57       52.17
                     Saint Lucia |      3,696        5.30       57.47
Saint Vincent and the Grenadines |      3,673        5.27       62.74
             Antigua and Barbuda |      3,366        4.83       67.57
                          Belize |      3,305        4.74       72.32
           Saint Kitts and Nevis |      3,057        4.39       76.70
                      Cape Verde |      2,678        3.84       80.55
           Sao Tome and Principe |      2,651        3.81       84.35
                   Liechtenstein |      1,703        2.44       86.80
                      Seychelles |      1,423        2.04       88.84
                      San Marino |      1,327        1.90       90.75
                          Monaco |      1,315        1.89       92.63
                         Andorra |      1,279        1.84       94.47
          Bosnia and Herzegovina |        893        1.28       95.75
                        Maldives |        607        0.87       96.62
                         Lebanon |        578        0.83       97.45
      German Democratic Republic |        232        0.33       97.78
                        Zimbabwe |        230        0.33       98.11
                          Brunei |        203        0.29       98.41
                            Peru |        162        0.23       98.64
                        Cambodia |        119        0.17       98.81
                           Ghana |        113        0.16       98.97
                         Vanuatu |        104        0.15       99.12
                         Tunisia |        101        0.14       99.27
                        Zanzibar |         90        0.13       99.39
                          Kuwait |         79        0.11       99.51
                          Uganda |         70        0.10       99.61
                         Hungary |         51        0.07       99.68
                     Afghanistan |         42        0.06       99.74
                Marshall Islands |         30        0.04       99.78
                           Syria |         27        0.04       99.82
                        Kiribati |         23        0.03       99.86
                      Bangladesh |         16        0.02       99.88
                           Palau |         16        0.02       99.90
                           Tonga |         15        0.02       99.92
                          Tuvalu |         14        0.02       99.94
                           Nauru |         12        0.02       99.96
             Republic of Vietnam |         12        0.02       99.98
                      Micronesia |         11        0.02       99.99
                            Laos |          4        0.01      100.00
---------------------------------+-----------------------------------
                           Total |     69,671      100.00

. *small countries not matched
. 
. keep if _merge==3
(69697 observations deleted)

. drop abbrev _merge majpow

. 
. 
. foreach x of varlist mid fmid war region lngdppc lnlifedeer lncap lnmilper lnmilex lnirst lnenergy lnupop lntpop
>  lntottr lnlifepen lnacli {
  2. rename `x' `x'1
  3. }

. 
. rename ccode ccode1

. rename ccode2 ccode

. sort ccode year

. 
. merge m:1 ccode year using "outdatatotal12111719.dta"
ccode was int now double

    Result                           # of obs.
    -----------------------------------------
    not matched                        40,018
        from master                    39,966  (_merge==1)
        from using                         52  (_merge==2)

    matched                           436,541  (_merge==3)
    -----------------------------------------

. 
. keep if _merge==3
(40018 observations deleted)

. drop abbrev _merge majpow

. 
. rename ccode ccode2

. 
. foreach x of varlist mid fmid war region lngdppc lnlifedeer lncap lnmilper lnmilex lnirst lnenergy lnupop lntpop
>  lntottr lnlifepen lnacli {
  2. rename `x' `x'2
  3. }

. 
. gen lnlifedeerl=min(lnlifedeer1, lnlifedeer2) if lnlifedeer1~=. & lnlifedeer2~=.

. gen lngdppcl=min(lngdppc1, lngdppc2) if lngdppc1~=. & lngdppc2~=.

. gen lncapl=min(lncap1, lncap2) if lncap1~=. & lncap2~=.

. gen lnmilperl=min(lnmilper1, lnmilper2) if lnmilper1~=. & lnmilper2~=.

. gen lnmilexl=min(lnmilex1, lnmilex2) if lnmilex1~=. & lnmilex2~=.

. gen lnirstl=min(lnirst1, lnirst2) if lnirst1~=. & lnirst2~=.

. gen lnenergyl=min(lnenergy1, lnenergy2) if lnenergy1~=. & lnenergy2~=.

. gen lnupopl=min(lnupop1, lnupop2) if lnupop1~=. & lnupop2~=.

. gen lntpopl=min(lntpop1, lntpop2) if lntpop1~=. & lntpop2~=.

. gen lntottrl=min(lntottr1, lntottr2) if lntottr1~=. & lntottr2~=.

. gen lnlifepenl=min(lnlifepen1, lnlifepen2) if lnlifepen1~=. & lnlifepen2~=.

. gen lnaclil=min(lnacli1, lnacli2) if lnacli1~=. & lnacli2~=.

. 
. gen lnlifedeerh=max(lnlifedeer1, lnlifedeer2) if lnlifedeer1~=. & lnlifedeer2~=.

. gen lngdppch=max(lngdppc1, lngdppc2) if lngdppc1~=. & lngdppc2~=.

. gen lncaph=max(lncap1, lncap2) if lncap1~=. & lncap2~=.

. gen lnmilperh=max(lnmilper1, lnmilper2) if lnmilper1~=. & lnmilper2~=.

. gen lnmilexh=max(lnmilex1, lnmilex2) if lnmilex1~=. & lnmilex2~=.

. gen lnirsth=max(lnirst1, lnirst2) if lnirst1~=. & lnirst2~=.

. gen lnenergyh=max(lnenergy1, lnenergy2) if lnenergy1~=. & lnenergy2~=.

. gen lnupoph=max(lnupop1, lnupop2) if lnupop1~=. & lnupop2~=.

. gen lntpoph=max(lntpop1, lntpop2) if lntpop1~=. & lntpop2~=.

. gen lntottrh=max(lntottr1, lntottr2) if lntottr1~=. & lntottr2~=.

. gen lnlifepenh=max(lnlifepen1, lnlifepen2) if lnlifepen1~=. & lnlifepen2~=.

. gen lnaclih=max(lnacli1, lnacli2) if lnacli1~=. & lnacli2~=.

. 
. *simplify dataset:
. keep country_name1 country_name2 ccode1 ccode2 year abbrev1 abbrev2 cwmid cwongo cwfatald dyadid lncprt mjpw dml
>  dmh contigl lndist polity22 contig distance numstate numGPs cwongonm cwmidnm cwpceyrs midonsl midongl fmidonsl 
> fmidongl warl midyears midyears2 midyears3 fmidyears fmidyears2 fmidyears3 waryears waryears2 waryears3 tpop MMC
> IE lnlifedeerl lngdppcl lnlifepenl lnlifedeerh lngdppch lnlifepenh

. 
. count
436541

. 
. gen m=19

. 
. sort ccode1 ccode2 year

. save "mi_19.dta", replace
(note: file mi_19.dta not found)
file mi_19.dta saved

. 
. 
. 
. 
. 
. *imputation 20
. clear

. use "12-05-28_dem_CIE_analysis.dta"

. 
. drop if year<1950
(0 observations deleted)

. drop if year>2001
(0 observations deleted)

. 
. rename ccode1 ccode

. sort ccode year

. merge m:1 ccode year using "outdatatotal12111720.dta"
ccode was int now double
year was int now double
numstate was int now long
numGPs was byte now long
polity2 was byte now long

    Result                           # of obs.
    -----------------------------------------
    not matched                        69,697
        from master                    69,671  (_merge==1)
        from using                         26  (_merge==2)

    matched                           476,507  (_merge==3)
    -----------------------------------------

. 
. tab country_name1 if _merge==1, sort

       COW CCode Num for state 1 |      Freq.     Percent        Cum.
---------------------------------+-----------------------------------
                      Luxembourg |      5,560        7.98        7.98
                        Barbados |      5,485        7.87       15.85
                         Bahamas |      4,776        6.86       22.71
                         Grenada |      4,418        6.34       29.05
                         Iceland |      4,173        5.99       35.04
                           Malta |      4,165        5.98       41.02
                        Dominica |      3,886        5.58       46.59
                        Suriname |      3,881        5.57       52.17
                     Saint Lucia |      3,696        5.30       57.47
Saint Vincent and the Grenadines |      3,673        5.27       62.74
             Antigua and Barbuda |      3,366        4.83       67.57
                          Belize |      3,305        4.74       72.32
           Saint Kitts and Nevis |      3,057        4.39       76.70
                      Cape Verde |      2,678        3.84       80.55
           Sao Tome and Principe |      2,651        3.81       84.35
                   Liechtenstein |      1,703        2.44       86.80
                      Seychelles |      1,423        2.04       88.84
                      San Marino |      1,327        1.90       90.75
                          Monaco |      1,315        1.89       92.63
                         Andorra |      1,279        1.84       94.47
          Bosnia and Herzegovina |        893        1.28       95.75
                        Maldives |        607        0.87       96.62
                         Lebanon |        578        0.83       97.45
      German Democratic Republic |        232        0.33       97.78
                        Zimbabwe |        230        0.33       98.11
                          Brunei |        203        0.29       98.41
                            Peru |        162        0.23       98.64
                        Cambodia |        119        0.17       98.81
                           Ghana |        113        0.16       98.97
                         Vanuatu |        104        0.15       99.12
                         Tunisia |        101        0.14       99.27
                        Zanzibar |         90        0.13       99.39
                          Kuwait |         79        0.11       99.51
                          Uganda |         70        0.10       99.61
                         Hungary |         51        0.07       99.68
                     Afghanistan |         42        0.06       99.74
                Marshall Islands |         30        0.04       99.78
                           Syria |         27        0.04       99.82
                        Kiribati |         23        0.03       99.86
                      Bangladesh |         16        0.02       99.88
                           Palau |         16        0.02       99.90
                           Tonga |         15        0.02       99.92
                          Tuvalu |         14        0.02       99.94
                           Nauru |         12        0.02       99.96
             Republic of Vietnam |         12        0.02       99.98
                      Micronesia |         11        0.02       99.99
                            Laos |          4        0.01      100.00
---------------------------------+-----------------------------------
                           Total |     69,671      100.00

. *small countries not matched
. 
. keep if _merge==3
(69697 observations deleted)

. drop abbrev _merge majpow

. 
. 
. foreach x of varlist mid fmid war region lngdppc lnlifedeer lncap lnmilper lnmilex lnirst lnenergy lnupop lntpop
>  lntottr lnlifepen lnacli {
  2. rename `x' `x'1
  3. }

. 
. rename ccode ccode1

. rename ccode2 ccode

. sort ccode year

. 
. merge m:1 ccode year using "outdatatotal12111720.dta"
ccode was int now double

    Result                           # of obs.
    -----------------------------------------
    not matched                        40,018
        from master                    39,966  (_merge==1)
        from using                         52  (_merge==2)

    matched                           436,541  (_merge==3)
    -----------------------------------------

. 
. keep if _merge==3
(40018 observations deleted)

. drop abbrev _merge majpow

. 
. rename ccode ccode2

. 
. foreach x of varlist mid fmid war region lngdppc lnlifedeer lncap lnmilper lnmilex lnirst lnenergy lnupop lntpop
>  lntottr lnlifepen lnacli {
  2. rename `x' `x'2
  3. }

. 
. gen lnlifedeerl=min(lnlifedeer1, lnlifedeer2) if lnlifedeer1~=. & lnlifedeer2~=.

. gen lngdppcl=min(lngdppc1, lngdppc2) if lngdppc1~=. & lngdppc2~=.

. gen lncapl=min(lncap1, lncap2) if lncap1~=. & lncap2~=.

. gen lnmilperl=min(lnmilper1, lnmilper2) if lnmilper1~=. & lnmilper2~=.

. gen lnmilexl=min(lnmilex1, lnmilex2) if lnmilex1~=. & lnmilex2~=.

. gen lnirstl=min(lnirst1, lnirst2) if lnirst1~=. & lnirst2~=.

. gen lnenergyl=min(lnenergy1, lnenergy2) if lnenergy1~=. & lnenergy2~=.

. gen lnupopl=min(lnupop1, lnupop2) if lnupop1~=. & lnupop2~=.

. gen lntpopl=min(lntpop1, lntpop2) if lntpop1~=. & lntpop2~=.

. gen lntottrl=min(lntottr1, lntottr2) if lntottr1~=. & lntottr2~=.

. gen lnlifepenl=min(lnlifepen1, lnlifepen2) if lnlifepen1~=. & lnlifepen2~=.

. gen lnaclil=min(lnacli1, lnacli2) if lnacli1~=. & lnacli2~=.

. 
. gen lnlifedeerh=max(lnlifedeer1, lnlifedeer2) if lnlifedeer1~=. & lnlifedeer2~=.

. gen lngdppch=max(lngdppc1, lngdppc2) if lngdppc1~=. & lngdppc2~=.

. gen lncaph=max(lncap1, lncap2) if lncap1~=. & lncap2~=.

. gen lnmilperh=max(lnmilper1, lnmilper2) if lnmilper1~=. & lnmilper2~=.

. gen lnmilexh=max(lnmilex1, lnmilex2) if lnmilex1~=. & lnmilex2~=.

. gen lnirsth=max(lnirst1, lnirst2) if lnirst1~=. & lnirst2~=.

. gen lnenergyh=max(lnenergy1, lnenergy2) if lnenergy1~=. & lnenergy2~=.

. gen lnupoph=max(lnupop1, lnupop2) if lnupop1~=. & lnupop2~=.

. gen lntpoph=max(lntpop1, lntpop2) if lntpop1~=. & lntpop2~=.

. gen lntottrh=max(lntottr1, lntottr2) if lntottr1~=. & lntottr2~=.

. gen lnlifepenh=max(lnlifepen1, lnlifepen2) if lnlifepen1~=. & lnlifepen2~=.

. gen lnaclih=max(lnacli1, lnacli2) if lnacli1~=. & lnacli2~=.

. 
. *simplify dataset:
. keep country_name1 country_name2 ccode1 ccode2 year abbrev1 abbrev2 cwmid cwongo cwfatald dyadid lncprt mjpw dml
>  dmh contigl lndist polity22 contig distance numstate numGPs cwongonm cwmidnm cwpceyrs midonsl midongl fmidonsl 
> fmidongl warl midyears midyears2 midyears3 fmidyears fmidyears2 fmidyears3 waryears waryears2 waryears3 tpop MMC
> IE lnlifedeerl lngdppcl lnlifepenl lnlifedeerh lngdppch lnlifepenh

. 
. count
436541

. 
. gen m=20

. 
. sort ccode1 ccode2 year

. save "mi_20.dta", replace
(note: file mi_20.dta not found)
file mi_20.dta saved

. 
. 
. 
. 
. 
. 
. **Code from: https://lists.gking.harvard.edu/pipermail/amelia/2012-February/000841.html
. * 2) Appending files together
. clear

. use "mi_0.dta"

. append using "mi_1.dta"
ccode1 was float now double
ccode2 was float now double
year was float now double
numstate was int now long
numGPs was byte now long
MMCIE was float now double

. append using "mi_2.dta"

. append using "mi_3.dta"

. append using "mi_4.dta"

. append using "mi_5.dta"

. append using "mi_6.dta"

. append using "mi_7.dta"

. append using "mi_8.dta"

. append using "mi_9.dta"

. append using "mi_10.dta"

. append using "mi_11.dta"

. append using "mi_12.dta"

. append using "mi_13.dta"

. append using "mi_14.dta"

. append using "mi_15.dta"

. append using "mi_16.dta"

. append using "mi_17.dta"

. append using "mi_18.dta"

. append using "mi_19.dta"

. append using "mi_20.dta"

. 
. *Check
. tab m

          m |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |    436,541        4.76        4.76
          1 |    436,541        4.76        9.52
          2 |    436,541        4.76       14.29
          3 |    436,541        4.76       19.05
          4 |    436,541        4.76       23.81
          5 |    436,541        4.76       28.57
          6 |    436,541        4.76       33.33
          7 |    436,541        4.76       38.10
          8 |    436,541        4.76       42.86
          9 |    436,541        4.76       47.62
         10 |    436,541        4.76       52.38
         11 |    436,541        4.76       57.14
         12 |    436,541        4.76       61.90
         13 |    436,541        4.76       66.67
         14 |    436,541        4.76       71.43
         15 |    436,541        4.76       76.19
         16 |    436,541        4.76       80.95
         17 |    436,541        4.76       85.71
         18 |    436,541        4.76       90.48
         19 |    436,541        4.76       95.24
         20 |    436,541        4.76      100.00
------------+-----------------------------------
      Total |  9,167,361      100.00

. 
. *order lnlifedeerl
. *browse if m==0
. *browse if m==1
. 
.  
. *Sorting
. sort m ccode1 ccode2 year

. 
. gen CIEl=lnlifedeerl
(398484 missing values generated)

. sum CIEl if m==1, d

                            CIEl
-------------------------------------------------------------
      Percentiles      Smallest
 1%     -.732757      -2.039853
 5%     .0774433      -2.039853
10%     .3621106      -2.039853       Obs              436541
25%      .907733      -2.039853       Sum of Wgt.      436541

50%     1.449198                      Mean           1.624743
                        Largest       Std. Dev.      1.185425
75%     2.148066       7.936787
90%     3.065909       7.936787       Variance       1.405233
95%     3.834805       7.936787       Skewness       1.110342
99%     5.791464       7.936787       Kurtosis       5.560453

. gen CIElc=CIEl-r(p75)
(398484 missing values generated)

. gen dmlCIElc=dml*CIElc
(398484 missing values generated)

. 
. * 3) Saving data (NOTE: Stata requires this step -- data must be saved before importing)
. save "midata", replace
file midata.dta saved

.  
.  
. forvalues j=0(1)20 { 
  2. erase "mi_`j'.dta"
  3. }

. 
. * 4) Importing into STATA as MI
. clear 

. use "midata" 

. mi import flong,  m(m) id(ccode1 ccode2 year) imputed(CIEl CIElc dmlCIElc lnlifedeerl lngdppcl lnlifepenl lnlife
> deerh lngdppch lnlifepenh)
(399587 m=0 obs. now marked as incomplete)

. *NOTE: Use the "imputed" to note which variables had missing data.  All imputed vars should be declared.
. 
. save "midata", replace
file midata.dta saved

. 
. mi convert wide

. save "midata", replace
file midata.dta saved

. 
. mi describe

  Style:  wide
          last mi update 23nov2012 08:35:28, 2 seconds ago

  Obs.:   complete       36,954
          incomplete    399,587  (M = 20 imputations)
          ---------------------
          total         436,541

  Vars.:  imputed:  9; CIEl(398484) CIElc(398484) dmlCIElc(398484) lnlifedeerl(398484) lngdppcl(15640)
                    lnlifepenl(398924) lnlifedeerh(398484) lngdppch(15640) lnlifepenh(398924)

          passive:  0

          regular:  0

          system:   1; _mi_miss

         (there are 42 unregistered variables)

. 
. mi varying

             Possible problem   variable names
  ----------------------------------------------------------------------------------------------------------------
           imputed nonvarying:  (none)
           passive nonvarying:  (none)
  ----------------------------------------------------------------------------------------------------------------

. *See STATA help on the import command re: what these commands should produce if data are properly identified
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
end of do-file

. 
. 
. ***Mousseau Analysis***
. *This file confirms that Mousseau 2013 analysis can be precisely replicated from these files
. clear

. cd "$filetree"
/Users/Allan/Dropbox/!!Papers/Liberal Peace/12-02-21_ISQ_commentary/DOR_ISQ_2013_Replication/M_Rep

. do "12-06-23_MM_analysis.do"

. 
. *********************************************************************************************************
. ******************************************ANALYSES*******************************************************
. *********************************************************************************************************
. 
. clear

. cd "$filetree"
/Users/Allan/Dropbox/!!Papers/Liberal Peace/12-02-21_ISQ_commentary/DOR_ISQ_2013_Replication/M_Rep

. *AD: 
. use "MM_precise.dta"

. *AD*use "C:\Users\mmousseau\Documents\Working Papers Data Sets\Cap Peace\DPUNRAV.dta", clear
. 
. 
. ********************************************************************************************
. ****Table 1: Contract Intensive Economy, Democracy, and Militarized Interstate Conflict*****
. ********************************************************************************************
. global fmcontrols lncprt mjpw cntg dist numstate fpceyrs fspl1 fspl2 fspl3

. global amcontrols lncprt mjpw cntg dist numstate apceyrs aspl1 aspl2 aspl3

. 
. quietly {

. 
. esttab, b(2) se(2) replace label star(t 0.10 * 0.05 ** 0.01 *** 0.001)  order(CIEl dml bdm h10dm dmlsq PolDis  $
> fmcontrols      $amcontrol) scalars("ll Log lik.") pr2 varwidth(25) modelwidth(8)

-------------------------------------------------------------------------------------------------
                               (1)         (2)         (3)         (4)         (5)         (6)   
                          MM Fat~d    MM Fat~d    MM Fat~d    MM Fat~d    MM MID~d    MM Fat~d   
-------------------------------------------------------------------------------------------------
CIEL                                     -0.80***    -0.85***    -0.83***    -0.29***    -0.84***
                                        (0.14)      (0.14)      (0.13)      (0.06)      (0.14)   

DemocracyL                   -0.08**      0.00        0.03                                       
                            (0.03)      (0.03)      (0.03)                                       

DemocracyBinary6                                                  0.63                           
                                                                (0.48)                           

DemocracyBinary10                                                            -0.41               
                                                                            (0.45)               

DemocracyL^2                                                                              0.00   
                                                                                        (0.00)   

PolDis                                                1.00***     1.00***     0.58***     1.01***
                                                    (0.23)      (0.22)      (0.10)      (0.24)   

Relative capability          -0.21**     -0.28***    -0.31***    -0.30***    -0.24***    -0.30***
                            (0.08)      (0.08)      (0.08)      (0.08)      (0.05)      (0.08)   

Major power                   1.31**      1.73***     1.84***     1.84***     1.99***     1.83***
                            (0.41)      (0.36)      (0.36)      (0.36)      (0.19)      (0.36)   

Contiguity                    3.94***     3.77***     3.82***     3.83***     2.80***     3.83***
                            (0.47)      (0.44)      (0.42)      (0.41)      (0.20)      (0.42)   

Distance                     -0.39**     -0.50***    -0.54***    -0.54***    -0.44***    -0.53***
                            (0.12)      (0.12)      (0.11)      (0.11)      (0.07)      (0.11)   

numstate                     -0.00       -0.00       -0.00       -0.00        0.00*      -0.00   
                            (0.00)      (0.00)      (0.00)      (0.00)      (0.00)      (0.00)   

Time since last mzfmidl      -0.30***    -0.27***    -0.26***    -0.26***                -0.26***
                            (0.05)      (0.05)      (0.05)      (0.05)                  (0.05)   

(fpceyrs-k1) cubed           -0.00*      -0.00*      -0.00*      -0.00*                  -0.00*  
                            (0.00)      (0.00)      (0.00)      (0.00)                  (0.00)   

(fpceyrs-k2) cubed            0.00        0.00        0.00        0.00                    0.00   
                            (0.00)      (0.00)      (0.00)      (0.00)                  (0.00)   

(fpceyrs-k3) cubed           -0.00       -0.00       -0.00       -0.00                   -0.00   
                            (0.00)      (0.00)      (0.00)      (0.00)                  (0.00)   

Time since last mzmidl                                                       -0.34***            
                                                                            (0.03)               

(apceyrs-k1) cubed                                                           -0.00***            
                                                                            (0.00)               

(apceyrs-k2) cubed                                                            0.00***            
                                                                            (0.00)               

(apceyrs-k3) cubed                                                           -0.00*              
                                                                            (0.00)               

Constant                     -3.74**     -1.71       -2.29*      -2.64*      -2.04**     -2.61*  
                            (1.23)      (1.24)      (1.16)      (1.19)      (0.66)      (1.19)   
-------------------------------------------------------------------------------------------------
Observations                321568      321568      301072      301072      301291      301072   
Pseudo R-squared             0.376       0.395       0.405       0.405       0.391       0.404   
Log lik.                  -1041.50    -1009.75     -924.63     -924.43    -3840.39     -924.85   
-------------------------------------------------------------------------------------------------
Standard errors in parentheses
t p<0.10, * p<0.05, ** p<0.01, *** p<0.001

. 
. 
. ********************************************************************************************
. ****Table A1: Contract Intensive Economy, Democracy, and Militarized Interstate Conflict****
. ********************************************************************************************
. quietly {

. 
. 
. 
. esttab, b(2) se(2) replace label star(t 0.10 * 0.05 ** 0.01 *** 0.001)  order(CIEl dml bdm dmlsq PolDis $amcontr
> ol) scalars("ll Log lik.") pr2 varwidth(25) modelwidth(8)

-------------------------------------------------------------
                               (1)         (2)         (3)   
                          MM MID~d    MM MID~d    MM MID~d   
-------------------------------------------------------------
CIEL                         -0.31***    -0.31***    -0.30***
                            (0.06)      (0.06)      (0.06)   

DemocracyL                   -0.00                           
                            (0.01)                           

DemocracyBinary6                         -0.09               
                                        (0.28)               

DemocracyL^2                                         -0.00   
                                                    (0.00)   

PolDis                        0.59***     0.59***     0.58***
                            (0.11)      (0.10)      (0.11)   

Relative capability          -0.24***    -0.24***    -0.24***
                            (0.05)      (0.05)      (0.05)   

Major power                   1.99***     1.99***     1.98***
                            (0.19)      (0.19)      (0.19)   

Contiguity                    2.80***     2.80***     2.80***
                            (0.21)      (0.21)      (0.21)   

Distance                     -0.44***    -0.44***    -0.44***
                            (0.07)      (0.07)      (0.07)   

numstate                      0.00*       0.00*       0.00*  
                            (0.00)      (0.00)      (0.00)   

Time since last mzmidl       -0.34***    -0.34***    -0.34***
                            (0.03)      (0.03)      (0.03)   

(apceyrs-k1) cubed           -0.00***    -0.00***    -0.00***
                            (0.00)      (0.00)      (0.00)   

(apceyrs-k2) cubed            0.00***     0.00***     0.00***
                            (0.00)      (0.00)      (0.00)   

(apceyrs-k3) cubed           -0.00*      -0.00*      -0.00*  
                            (0.00)      (0.00)      (0.00)   

Constant                     -2.09**     -2.06**     -2.07** 
                            (0.65)      (0.67)      (0.66)   
-------------------------------------------------------------
Observations                301291      301291      301291   
Pseudo R-squared             0.391       0.391       0.391   
Log lik.                  -3841.34    -3841.22    -3841.09   
-------------------------------------------------------------
Standard errors in parentheses
t p<0.10, * p<0.05, ** p<0.01, *** p<0.001

. 
. 
. ********************************************************************************************
. ****************Table 2: Tests for Competing Theories of Capitalist Peace ******************
. ********************************************************************************************
. quietly {

. 
. esttab, b(2) se(2) replace label star(t 0.10 * 0.05 ** 0.01 *** 0.001)  order(CIEl edvl dpl capopenl_ipol2 pubh 
> PolDis $fmcontrols) scalars("ll Log lik.") pr2 varwidth(25) modelwidth(8)

-------------------------------------------------------------------------
                               (1)         (2)         (3)         (4)   
                          MM Fat~d    MM Fat~d    MM Fat~d    MM Fat~d   
-------------------------------------------------------------------------
CIEL                         -0.70***    -0.68***    -0.67***    -0.72***
                            (0.15)      (0.15)      (0.14)      (0.19)   

WealthL                      -0.30                                       
                            (0.26)                                       

TradeL                                   -1.18t                          
                                        (0.65)                           

Capital OpennessL                                    -0.06               
                                                    (0.07)               

PublicH                                                          -0.02** 
                                                                (0.01)   

PolDis                        0.92***     0.88***     0.80***     1.20***
                            (0.21)      (0.21)      (0.22)      (0.31)   

Relative capability          -0.30***    -0.34***    -0.29**     -0.11   
                            (0.09)      (0.09)      (0.09)      (0.13)   

Major power                   1.93***     1.98***     1.43***     0.97   
                            (0.37)      (0.36)      (0.40)      (0.68)   

Contiguity                    3.81***     3.83***     4.09***     4.94***
                            (0.41)      (0.41)      (0.45)      (0.57)   

Distance                     -0.55***    -0.56***    -0.49***    -0.32** 
                            (0.11)      (0.10)      (0.10)      (0.12)   

numstate                     -0.00       -0.00        0.02**     -0.02*  
                            (0.00)      (0.00)      (0.01)      (0.01)   

Time since last mzfmidl      -0.26***    -0.25***    -0.23***    -0.13t  
                            (0.05)      (0.05)      (0.06)      (0.08)   

(fpceyrs-k1) cubed           -0.00*      -0.00*      -0.00        0.00   
                            (0.00)      (0.00)      (0.00)      (0.00)   

(fpceyrs-k2) cubed            0.00        0.00t       0.00       -0.00   
                            (0.00)      (0.00)      (0.00)      (0.00)   

(fpceyrs-k3) cubed           -0.00       -0.00        0.00        0.00   
                            (0.00)      (0.00)      (0.00)      (0.00)   

Constant                     -2.72*      -2.41*      -5.73***    -1.79   
                            (1.23)      (1.17)      (1.52)      (2.06)   
-------------------------------------------------------------------------
Observations                301072      296553      191914      115469   
Pseudo R-squared             0.405       0.409       0.435       0.450   
Log lik.                   -924.47     -902.55     -547.04     -244.51   
-------------------------------------------------------------------------
Standard errors in parentheses
t p<0.10, * p<0.05, ** p<0.01, *** p<0.001

. 
. 
. 
. ********************************************************************************************
. ***************Table A2: Tests for Competing Theories of Capitalist Peace ******************
. ********************************************************************************************
. quietly {

. 
. esttab, b(2) se(2) replace label star(t 0.10 * 0.05 ** 0.01 *** 0.001)  order(edvl dpl capopenl_ipol2 pubh PolDi
> s $fmcontrols) scalars("ll Log lik.") pr2 varwidth(25) modelwidth(8)

-------------------------------------------------------------------------
                               (1)         (2)         (3)         (4)   
                          MM Fat~d    MM Fat~d    MM Fat~d    MM Fat~d   
-------------------------------------------------------------------------
WealthL                      -1.05***                                    
                            (0.25)                                       

TradeL                                   -2.02**                         
                                        (0.67)                           

Capital OpennessL                                    -0.06               
                                                    (0.05)               

PublicH                                                          -0.00   
                                                                (0.01)   

PolDis                        1.05***     0.96***     1.00***     1.51***
                            (0.20)      (0.21)      (0.25)      (0.34)   

Relative capability          -0.30***    -0.37***    -0.34***    -0.22   
                            (0.08)      (0.08)      (0.09)      (0.14)   

Major power                   2.04***     2.01***     1.21***     0.38   
                            (0.34)      (0.34)      (0.37)      (0.67)   

Contiguity                    4.07***     4.06***     4.52***     5.28***
                            (0.38)      (0.38)      (0.49)      (0.66)   

Distance                     -0.50***    -0.54***    -0.35**     -0.14   
                            (0.11)      (0.10)      (0.13)      (0.16)   

numstate                      0.00       -0.00        0.01       -0.02*  
                            (0.00)      (0.00)      (0.01)      (0.01)   

Time since last mzfmidl      -0.28***    -0.26***    -0.24***    -0.13t  
                            (0.05)      (0.04)      (0.06)      (0.07)   

(fpceyrs-k1) cubed           -0.00**     -0.00*      -0.00        0.00   
                            (0.00)      (0.00)      (0.00)      (0.00)   

(fpceyrs-k2) cubed            0.00*       0.00*       0.00       -0.00   
                            (0.00)      (0.00)      (0.00)      (0.00)   

(fpceyrs-k3) cubed           -0.00t      -0.00t      -0.00       -0.00   
                            (0.00)      (0.00)      (0.00)      (0.00)   

Constant                     -4.36***    -3.19**     -6.23***    -4.89*  
                            (1.14)      (1.18)      (1.64)      (2.10)   
-------------------------------------------------------------------------
Observations                364818      359812      238173      126403   
Pseudo R-squared             0.384       0.387       0.394       0.418   
Log lik.                  -1126.26    -1104.98     -675.07     -284.39   
-------------------------------------------------------------------------
Standard errors in parentheses
t p<0.10, * p<0.05, ** p<0.01, *** p<0.001

. 
. 
. 
. ********************************************************************************************
. 
. ***************Table A3: Tests for Competing Theories of Capitalist Peace ******************
. 
. ********************************************************************************************
. 
. quietly {

. 
. esttab, b(2) se(2) replace label star(t 0.10 * 0.05 ** 0.01 *** 0.001)  order(CIEl dvl edvl dpl capopenl_ipol2 p
> ubh PolDis $fmcontrols $amcontrols) scalars("ll Log lik.") pr2 varwidth(25) modelwidth(8)

-------------------------------------------------------------------------------------------------
                               (1)         (2)         (3)         (4)         (5)         (6)   
                          MM Fat~d    MM MID~d    MM MID~d    MM MID~d    MM MID~d    MM MID~d   
-------------------------------------------------------------------------------------------------
CIEL                         -0.65***    -0.42***    -0.37***    -0.27***    -0.22***    -0.26***
                            (0.16)      (0.06)      (0.06)      (0.06)      (0.06)      (0.06)   

dvl                          -0.23        0.22**                                                 
                            (0.16)      (0.08)                                                   

WealthL                                               0.27**                                     
                                                    (0.10)                                       

TradeL                                                           -0.20                           
                                                                (0.13)                           

Capital OpennessL                                                            -0.02               
                                                                            (0.03)               

PublicH                                                                                   0.00   
                                                                                        (0.00)   

PolDis                        0.96***     0.57***     0.60***     0.59***     0.47***     0.39*  
                            (0.21)      (0.10)      (0.10)      (0.10)      (0.11)      (0.15)   

Relative capability          -0.28**     -0.25***    -0.23***    -0.26***    -0.19***    -0.15*  
                            (0.09)      (0.05)      (0.05)      (0.05)      (0.05)      (0.08)   

Major power                   1.88***     1.91***     1.87***     2.05***     1.79***     1.86***
                            (0.38)      (0.19)      (0.18)      (0.19)      (0.21)      (0.34)   

Contiguity                    3.81***     2.78***     2.79***     2.81***     2.67***     3.53***
                            (0.41)      (0.20)      (0.20)      (0.20)      (0.25)      (0.33)   

Distance                     -0.56***    -0.43***    -0.43***    -0.45***    -0.40***    -0.12   
                            (0.11)      (0.07)      (0.07)      (0.07)      (0.08)      (0.10)   

numstate                     -0.00        0.00        0.00        0.00*       0.01*      -0.02***
                            (0.00)      (0.00)      (0.00)      (0.00)      (0.00)      (0.01)   

Time since last mzfmidl      -0.26***                                                            
                            (0.05)                                                               

(fpceyrs-k1) cubed           -0.00*                                                              
                            (0.00)                                                               

(fpceyrs-k2) cubed            0.00                                                               
                            (0.00)                                                               

(fpceyrs-k3) cubed           -0.00                                                               
                            (0.00)                                                               

Time since last mzmidl                   -0.34***    -0.34***    -0.34***    -0.35***    -0.41***
                                        (0.03)      (0.03)      (0.03)      (0.04)      (0.05)   

(apceyrs-k1) cubed                       -0.00***    -0.00***    -0.00***    -0.00**     -0.00***
                                        (0.00)      (0.00)      (0.00)      (0.00)      (0.00)   

(apceyrs-k2) cubed                        0.00**      0.00***     0.00**      0.00t       0.00*  
                                        (0.00)      (0.00)      (0.00)      (0.00)      (0.00)   

(apceyrs-k3) cubed                       -0.00t      -0.00*      -0.00t      -0.00       -0.00   
                                        (0.00)      (0.00)      (0.00)      (0.00)      (0.00)   

Constant                     -1.04       -3.46***    -1.98**     -2.01**     -2.50**     -0.56   
                            (1.62)      (0.86)      (0.64)      (0.66)      (0.94)      (1.32)   
-------------------------------------------------------------------------------------------------
Observations                301072      301291      301291      296767      192045      115504   
Pseudo R-squared             0.405       0.393       0.392       0.392       0.394       0.398   
Log lik.                   -923.67    -3831.73    -3835.01    -3769.66    -2297.01    -1176.63   
-------------------------------------------------------------------------------------------------
Standard errors in parentheses
t p<0.10, * p<0.05, ** p<0.01, *** p<0.001

. 
. 
. 
. 
. 
. 
. **********************************************************************
. 
. ***************TESTING FOR MULTICOLLINEARITY**************************
. 
. **********************************************************************
. 
. tab bCIE if bdm==1

  CIEBinary |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |     32,906       81.73       81.73
          1 |      7,357       18.27      100.00
------------+-----------------------------------
      Total |     40,263      100.00

. 
. corr CIEl dml if mzfmidl~=.  
(obs=321568)

             |     CIEl      dml
-------------+------------------
        CIEl |   1.0000
         dml |   0.4688   1.0000


. 
. reg mzfmidl     CIEl    dml                                             $fmcontrols

      Source |       SS       df       MS              Number of obs =  321568
-------------+------------------------------           F( 11,321556) =  381.29
       Model |  2.56063153    11  .232784684           Prob > F      =  0.0000
    Residual |  196.316219321556   .00061052           R-squared     =  0.0129
-------------+------------------------------           Adj R-squared =  0.0128
       Total |   198.87685321567  .000618462           Root MSE      =  .02471

------------------------------------------------------------------------------
     mzfmidl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        CIEl |   -.000378   .0000442    -8.54   0.000    -.0004647   -.0002912
         dml |  -.0000155   8.70e-06    -1.79   0.074    -.0000326    1.52e-06
      lncprt |    -.00016   .0000331    -4.83   0.000    -.0002249    -.000095
        mjpw |   .0010041   .0001721     5.83   0.000     .0006668    .0013414
        cntg |   .0127508   .0002599    49.07   0.000     .0122414    .0132601
        dist |  -.0002478   .0000659    -3.76   0.000     -.000377   -.0001187
    numstate |  -2.95e-06   2.35e-06    -1.26   0.208    -7.55e-06    1.65e-06
     fpceyrs |  -.0007415   .0000463   -16.02   0.000    -.0008322   -.0006507
       fspl1 |  -5.20e-06   4.79e-07   -10.85   0.000    -6.14e-06   -4.26e-06
       fspl2 |   2.89e-06   3.40e-07     8.49   0.000     2.22e-06    3.56e-06
       fspl3 |  -4.29e-07   1.04e-07    -4.14   0.000    -6.33e-07   -2.26e-07
       _cons |   .0074741    .000708    10.56   0.000     .0060864    .0088618
------------------------------------------------------------------------------

. 
. vif

    Variable |       VIF       1/VIF  
-------------+----------------------
       fspl2 |  10857.54    0.000092
       fspl1 |   7969.03    0.000125
       fspl3 |    962.86    0.001039
     fpceyrs |    200.20    0.004995
         dml |      1.46    0.683921
    numstate |      1.45    0.689999
        dist |      1.37    0.729355
        cntg |      1.35    0.741482
        CIEl |      1.34    0.747259
      lncprt |      1.23    0.812617
        mjpw |      1.23    0.816213
-------------+----------------------
    Mean VIF |   1818.10

. 
. reg mzfmidl CIEl edvl                                                           $fmcontrols

      Source |       SS       df       MS              Number of obs =  328181
-------------+------------------------------           F( 11,328169) =  386.19
       Model |  2.55435428    11  .232214025           Prob > F      =  0.0000
    Residual |  197.323762328169  .000601287           R-squared     =  0.0128
-------------+------------------------------           Adj R-squared =  0.0127
       Total |  199.878116328180   .00060905           Root MSE      =  .02452

------------------------------------------------------------------------------
     mzfmidl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        CIEl |  -.0003407   .0000465    -7.33   0.000    -.0004317   -.0002496
        edvl |  -.0002952   .0001193    -2.48   0.013     -.000529   -.0000615
      lncprt |  -.0001639   .0000326    -5.04   0.000    -.0002277   -.0001001
        mjpw |   .0010443   .0001715     6.09   0.000     .0007081    .0013805
        cntg |   .0126942   .0002561    49.57   0.000     .0121922    .0131961
        dist |  -.0002708   .0000651    -4.16   0.000    -.0003983   -.0001433
    numstate |  -3.09e-06   2.28e-06    -1.35   0.176    -7.56e-06    1.38e-06
     fpceyrs |  -.0007153   .0000454   -15.74   0.000    -.0008043   -.0006262
       fspl1 |  -5.07e-06   4.72e-07   -10.76   0.000    -6.00e-06   -4.15e-06
       fspl2 |   2.83e-06   3.35e-07     8.46   0.000     2.18e-06    3.49e-06
       fspl3 |  -4.26e-07   1.02e-07    -4.18   0.000    -6.26e-07   -2.26e-07
       _cons |   .0076631   .0006855    11.18   0.000     .0063195    .0090067
------------------------------------------------------------------------------

. 
. vif

    Variable |       VIF       1/VIF  
-------------+----------------------
       fspl2 |  11083.04    0.000090
       fspl1 |   8126.25    0.000123
       fspl3 |    980.92    0.001019
     fpceyrs |    202.65    0.004935
        edvl |      1.59    0.629326
        CIEl |      1.52    0.655841
    numstate |      1.42    0.704310
        dist |      1.39    0.721389
        cntg |      1.34    0.744138
        mjpw |      1.25    0.799994
      lncprt |      1.24    0.806878
-------------+----------------------
    Mean VIF |   1854.78

. 
. reg mzfmidl CIEl                dpl                                             $fmcontrols

      Source |       SS       df       MS              Number of obs =  323080
-------------+------------------------------           F( 11,323068) =  404.87
       Model |  2.67711254    11  .243373867           Prob > F      =  0.0000
    Residual |  194.202766323068   .00060112           R-squared     =  0.0136
-------------+------------------------------           Adj R-squared =  0.0136
       Total |  196.879878323079  .000609386           Root MSE      =  .02452

------------------------------------------------------------------------------
     mzfmidl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        CIEl |  -.0002147   .0000414    -5.19   0.000    -.0002958   -.0001336
         dpl |  -.0027091   .0001856   -14.59   0.000     -.003073   -.0023453
      lncprt |   -.000207   .0000329    -6.29   0.000    -.0002715   -.0001425
        mjpw |   .0011956   .0001713     6.98   0.000     .0008599    .0015313
        cntg |   .0135312   .0002635    51.35   0.000     .0130147    .0140476
        dist |  -.0004011   .0000658    -6.10   0.000      -.00053   -.0002721
    numstate |  -4.14e-06   2.30e-06    -1.80   0.072    -8.65e-06    3.72e-07
     fpceyrs |  -.0007348   .0000461   -15.92   0.000    -.0008252   -.0006443
       fspl1 |  -5.24e-06   4.78e-07   -10.95   0.000    -6.17e-06   -4.30e-06
       fspl2 |   2.94e-06   3.39e-07     8.67   0.000     2.27e-06    3.60e-06
       fspl3 |  -4.48e-07   1.03e-07    -4.37   0.000    -6.49e-07   -2.47e-07
       _cons |   .0089149    .000695    12.83   0.000     .0075527    .0102771
------------------------------------------------------------------------------

. 
. vif

    Variable |       VIF       1/VIF  
-------------+----------------------
       fspl2 |  11251.31    0.000089
       fspl1 |   8271.25    0.000121
       fspl3 |    988.99    0.001011
     fpceyrs |    205.96    0.004855
    numstate |      1.42    0.704219
        dist |      1.39    0.721083
        cntg |      1.38    0.726176
         dpl |      1.25    0.802033
      lncprt |      1.24    0.806581
        mjpw |      1.24    0.809033
        CIEl |      1.17    0.854587
-------------+----------------------
    Mean VIF |   1884.24

. 
. reg mzfmidl CIEl                                capopenl_ipol2          $fmcontrols

      Source |       SS       df       MS              Number of obs =  206800
-------------+------------------------------           F( 11,206788) =  284.17
       Model |  1.87511779    11  .170465254           Prob > F      =  0.0000
    Residual |  124.048112206788  .000599881           R-squared     =  0.0149
-------------+------------------------------           Adj R-squared =  0.0148
       Total |   125.92323206799  .000608916           Root MSE      =  .02449

--------------------------------------------------------------------------------
       mzfmidl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
          CIEl |  -.0003414   .0000542    -6.30   0.000    -.0004477   -.0002351
capopenl_ipol2 |  -.0000387   .0000317    -1.22   0.221    -.0001008    .0000233
        lncprt |  -.0001971   .0000404    -4.88   0.000    -.0002763   -.0001179
          mjpw |   .0008474   .0002178     3.89   0.000     .0004205    .0012743
          cntg |   .0127288   .0003215    39.59   0.000     .0120986     .013359
          dist |  -.0003655   .0000823    -4.44   0.000    -.0005268   -.0002041
      numstate |   .0000231   5.14e-06     4.51   0.000     .0000131    .0000332
       fpceyrs |  -.0011057   .0000663   -16.68   0.000    -.0012356   -.0009758
         fspl1 |  -7.10e-06   6.61e-07   -10.73   0.000    -8.40e-06   -5.80e-06
         fspl2 |   3.79e-06   4.75e-07     7.99   0.000     2.86e-06    4.72e-06
         fspl3 |  -4.91e-07   1.57e-07    -3.13   0.002    -7.99e-07   -1.83e-07
         _cons |   .0077063   .0010988     7.01   0.000     .0055527    .0098599
--------------------------------------------------------------------------------

. 
. vif

    Variable |       VIF       1/VIF  
-------------+----------------------
       fspl2 |   7873.22    0.000127
       fspl1 |   5899.78    0.000169
       fspl3 |    742.97    0.001346
     fpceyrs |    174.72    0.005724
    numstate |      1.53    0.651856
        dist |      1.37    0.728602
        cntg |      1.35    0.738142
      lncprt |      1.24    0.806339
        mjpw |      1.22    0.819061
        CIEl |      1.20    0.830484
capopenl_i~2 |      1.18    0.847723
-------------+----------------------
    Mean VIF |   1336.34

. 
. reg mzfmidl CIEl                                                        pubh    $fmcontrols

      Source |       SS       df       MS              Number of obs =  123626
-------------+------------------------------           F( 11,123614) =  124.45
       Model |  .580260212    11  .052750928           Prob > F      =  0.0000
    Residual |   52.397018123614  .000423876           R-squared     =  0.0110
-------------+------------------------------           Adj R-squared =  0.0109
       Total |  52.9772782123625  .000428532           Root MSE      =  .02059

------------------------------------------------------------------------------
     mzfmidl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        CIEl |  -.0003046   .0000478    -6.38   0.000    -.0003982    -.000211
        pubh |  -7.51e-07   2.91e-06    -0.26   0.796    -6.46e-06    4.96e-06
      lncprt |   -.000021   .0000442    -0.48   0.635    -.0001075    .0000656
        mjpw |  -.0001342   .0002337    -0.57   0.566    -.0005922    .0003238
        cntg |   .0098267   .0003558    27.62   0.000     .0091294    .0105241
        dist |   -.000154   .0000874    -1.76   0.078    -.0003254    .0000173
    numstate |  -.0000241   4.51e-06    -5.35   0.000     -.000033   -.0000153
     fpceyrs |  -.0008221   .0000819   -10.04   0.000    -.0009825   -.0006616
       fspl1 |  -5.03e-06   7.68e-07    -6.55   0.000    -6.53e-06   -3.52e-06
       fspl2 |   2.51e-06   5.14e-07     4.87   0.000     1.50e-06    3.51e-06
       fspl3 |  -2.21e-07   1.41e-07    -1.57   0.117    -4.96e-07    5.51e-08
       _cons |   .0111207   .0011798     9.43   0.000     .0088083    .0134331
------------------------------------------------------------------------------

. 
. vif

    Variable |       VIF       1/VIF  
-------------+----------------------
       fspl2 |  16057.23    0.000062
       fspl1 |  12839.80    0.000078
       fspl3 |   1187.23    0.000842
     fpceyrs |    344.23    0.002905
    numstate |      1.46    0.684203
        dist |      1.38    0.725364
        cntg |      1.35    0.738669
        mjpw |      1.18    0.846804
      lncprt |      1.16    0.858420
        CIEl |      1.13    0.883025
        pubh |      1.05    0.948317
-------------+----------------------
    Mean VIF |   2767.02

. 
. 
. 
. ***********************************************************************
. 
. *******************QUANTITIES OF INTEREST******************************
. 
. ***********************************************************************
. 
. clear

. cd "$filetree"
/Users/Allan/Dropbox/!!Papers/Liberal Peace/12-02-21_ISQ_commentary/DOR_ISQ_2013_Replication/M_Rep

. *AD: 
. use "MM_precise.dta"

. *AD*use "C:\Users\mmousseau\Documents\Working Papers Data Sets\Cap Peace\DPUNRAV.dta", clear
. 
. estsimp logit mzfmidl CIEl lncprt mjpw cntg dist numstate fpceyrs fspl1 fspl2 fspl3     , cl(ID) nolog

Logistic regression                               Number of obs   =     328181
                                                  Wald chi2(10)   =     984.63
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -1018.0515                 Pseudo R2       =     0.3942

                                 (Std. Err. adjusted for 12795 clusters in ID)
------------------------------------------------------------------------------
             |               Robust
     mzfmidl |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        CIEl |  -.7819249   .1239344    -6.31   0.000    -1.024832    -.539018
      lncprt |  -.2786368   .0834763    -3.34   0.001    -.4422472   -.1150264
        mjpw |   1.727135   .3723042     4.64   0.000     .9974325    2.456838
        cntg |    3.79423    .438457     8.65   0.000      2.93487     4.65359
        dist |   -.500941   .1196885    -4.19   0.000    -.7355262   -.2663559
    numstate |  -.0040089   .0031643    -1.27   0.205    -.0102108    .0021931
     fpceyrs |  -.2734159   .0472502    -5.79   0.000    -.3660246   -.1808072
       fspl1 |    -.00151   .0006548    -2.31   0.021    -.0027933   -.0002267
       fspl2 |   .0009728   .0005662     1.72   0.086    -.0001369    .0020824
       fspl3 |  -.0003071   .0002265    -1.36   0.175     -.000751    .0001368
       _cons |  -1.783955   1.297694    -1.37   0.169    -4.327388    .7594785
------------------------------------------------------------------------------

Simulating main parameters.  Please wait....
% of simulations completed: 9% 18% 27% 36% 45% 54% 63% 72% 81% 90% 100% 

Number of simulations  : 1000
Names of new variables : b1 b2 b3 b4 b5 b6 b7 b8 b9 b10 b11

. 
. setx CIEl median lncprt median mjpw 0 cntg 1 dist min numstate median fpceyrs 0 fspl1 0 fspl2 0 fspl3 0

. 
. 
. 
. simqi, fd(prval(1)) changex(CIEl p5 p95)

First Difference: CIEl p5 p95

      Quantity of Interest |     Mean       Std. Err.    [95% Conf. Interval]
---------------------------+--------------------------------------------------
          dPr(mzfmidl = 1) |   -.425636     .0979504    -.6011232    -.220474

. 
. simqi, fd(prval(1)) changex(lncprt p5 p95)

First Difference: lncprt p5 p95

      Quantity of Interest |     Mean       Std. Err.    [95% Conf. Interval]
---------------------------+--------------------------------------------------
          dPr(mzfmidl = 1) |  -.2583222     .0810932     -.424063   -.1030986

. 
. simqi, fd(prval(1)) changex(mjpw 0 1)

First Difference: mjpw 0 1

      Quantity of Interest |     Mean       Std. Err.    [95% Conf. Interval]
---------------------------+--------------------------------------------------
          dPr(mzfmidl = 1) |   .3712888     .0732775     .2183316    .5050606

. 
. simqi, fd(prval(1)) changex(cntg 0 1)

First Difference: cntg 0 1

      Quantity of Interest |     Mean       Std. Err.    [95% Conf. Interval]
---------------------------+--------------------------------------------------
          dPr(mzfmidl = 1) |   .3477572     .1034054     .1604255    .5480155

. 
. simqi, fd(prval(1)) changex(dist p5 p95)

First Difference: dist p5 p95

      Quantity of Interest |     Mean       Std. Err.    [95% Conf. Interval]
---------------------------+--------------------------------------------------
          dPr(mzfmidl = 1) |  -.0298384     .0076351    -.0458406   -.0164191

. 
. simqi, fd(prval(1)) changex(fpceyrs p5 p95)

First Difference: fpceyrs p5 p95

      Quantity of Interest |     Mean       Std. Err.    [95% Conf. Interval]
---------------------------+--------------------------------------------------
          dPr(mzfmidl = 1) |  -.2537933      .093417    -.4674091   -.1041911

. 
. 
. 
. 
. 
. ******************************
. 
. ****Table 2, Model 2**********
. 
. ******************************
. 
. clear

. cd "$filetree"
/Users/Allan/Dropbox/!!Papers/Liberal Peace/12-02-21_ISQ_commentary/DOR_ISQ_2013_Replication/M_Rep

. *AD: 
. use "MM_precise.dta"

. *AD*use "C:\Users\mmousseau\Documents\Working Papers Data Sets\Cap Peace\DPUNRAV.dta", clear
. 
. global fmcontrols PolDis lncprt mjpw cntg dist numstate fpceyrs fspl1 fspl2 fspl3

. 
. estsimp logit mzfmidl CIEl              dpl                                             $fmcontrols             
> , cl(ID) nolog

Logistic regression                               Number of obs   =     296553
                                                  Wald chi2(12)   =    1018.13
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -902.54743                 Pseudo R2       =     0.4093

                                 (Std. Err. adjusted for 11981 clusters in ID)
------------------------------------------------------------------------------
             |               Robust
     mzfmidl |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        CIEl |  -.6814465   .1494976    -4.56   0.000    -.9744564   -.3884366
         dpl |  -1.175474    .653614    -1.80   0.072    -2.456534    .1055859
      PolDis |   .8795861   .2065579     4.26   0.000     .4747401    1.284432
      lncprt |  -.3396764    .085355    -3.98   0.000    -.5069692   -.1723837
        mjpw |   1.984281   .3644265     5.44   0.000     1.270018    2.698544
        cntg |   3.827105   .4099418     9.34   0.000     3.023633    4.630576
        dist |  -.5630497   .0991703    -5.68   0.000      -.75742   -.3686794
    numstate |  -.0016422   .0034644    -0.47   0.635    -.0084322    .0051479
     fpceyrs |  -.2514884   .0486737    -5.17   0.000     -.346887   -.1560898
       fspl1 |  -.0014126   .0006932    -2.04   0.042    -.0027713   -.0000539
       fspl2 |   .0010067   .0006023     1.67   0.095    -.0001738    .0021872
       fspl3 |  -.0003755   .0002421    -1.55   0.121    -.0008499    .0000989
       _cons |  -2.406056   1.166515    -2.06   0.039    -4.692383   -.1197284
------------------------------------------------------------------------------
Note: 42 failures and 0 successes completely determined.

Simulating main parameters.  Please wait....
% of simulations completed: 7% 15% 23% 30% 38% 46% 53% 61% 69% 76% 84% 92% 100% 

Number of simulations  : 1000
Names of new variables : b1 b2 b3 b4 b5 b6 b7 b8 b9 b10 b11 b12 b13

. 
. setx CIEl median PolDis median dpl median lncprt median mjpw 0 cntg 1 dist min numstate median fpceyrs 0 fspl1 0
>  fspl2 0 fspl3 0

. 
. 
. 
. simqi, fd(prval(1)) changex(CIEl p5 p95)

First Difference: CIEl p5 p95

      Quantity of Interest |     Mean       Std. Err.    [95% Conf. Interval]
---------------------------+--------------------------------------------------
          dPr(mzfmidl = 1) |    -.44332     .0877451    -.6068059   -.2711145

. 
. simqi, fd(prval(1)) changex(dpl p5 p95)

First Difference: dpl p5 p95

      Quantity of Interest |     Mean       Std. Err.    [95% Conf. Interval]
---------------------------+--------------------------------------------------
          dPr(mzfmidl = 1) |   -.054008     .0311813    -.1125403    .0058649

. 
. simqi, fd(prval(1)) changex(PolDis p5 p95)

First Difference: PolDis p5 p95

      Quantity of Interest |     Mean       Std. Err.    [95% Conf. Interval]
---------------------------+--------------------------------------------------
          dPr(mzfmidl = 1) |   .3574617     .0795281     .1966256    .5061089

. 
. simqi, fd(prval(1)) changex(lncprt p5 p95)

First Difference: lncprt p5 p95

      Quantity of Interest |     Mean       Std. Err.    [95% Conf. Interval]
---------------------------+--------------------------------------------------
          dPr(mzfmidl = 1) |  -.3414723     .0787159    -.4924482    -.185638

. 
. simqi, fd(prval(1)) changex(mjpw 0 1)

First Difference: mjpw 0 1

      Quantity of Interest |     Mean       Std. Err.    [95% Conf. Interval]
---------------------------+--------------------------------------------------
          dPr(mzfmidl = 1) |   .3823552     .0657271     .2426267    .5063189

. 
. simqi, fd(prval(1)) changex(cntg 0 1)

First Difference: cntg 0 1

      Quantity of Interest |     Mean       Std. Err.    [95% Conf. Interval]
---------------------------+--------------------------------------------------
          dPr(mzfmidl = 1) |   .4402086     .0916175     .2546478     .625507

. 
. simqi, fd(prval(1)) changex(dist p5 p95)

First Difference: dist p5 p95

      Quantity of Interest |     Mean       Std. Err.    [95% Conf. Interval]
---------------------------+--------------------------------------------------
          dPr(mzfmidl = 1) |  -.0358597      .009641    -.0570831   -.0200615

. 
. simqi, fd(prval(1)) changex(fpceyrs p5 p95)

First Difference: fpceyrs p5 p95

      Quantity of Interest |     Mean       Std. Err.    [95% Conf. Interval]
---------------------------+--------------------------------------------------
          dPr(mzfmidl = 1) |   -.293613      .083092    -.4763885    -.147956

. 
. 
. 
. 
. ************AD CODE***********
. ******************************
. 
. ****Table 1, with corrected DV********
. 
. ******************************
. **Returns significance to Model 2
. 
. global fmcontrols lncprt mjpw cntg dist numstate fpceyrs fspl1 fspl2 fspl3

. global amcontrols lncprt mjpw cntg dist numstate apceyrs aspl1 aspl2 aspl3

. 
. quietly {

. 
. esttab, b(2) se(2) replace label star(t 0.10 * 0.05 ** 0.01 *** 0.001)  order(CIEl dml bdm h10dm dmlsq PolDis  $
> fmcontrols      $amcontrol) scalars("ll Log lik.") pr2 varwidth(25) modelwidth(8)

-------------------------------------------------------------------------------------------------
                               (1)         (2)         (3)         (4)         (5)         (6)   
                          Fatal ~d    Fatal ~d    Fatal ~d    Fatal ~d    MID On~d    Fatal ~d   
-------------------------------------------------------------------------------------------------
CIEL                                     -0.68***    -0.73***    -0.80***    -0.32***    -0.73***
                                        (0.11)      (0.11)      (0.11)      (0.06)      (0.11)   

DemocracyL                   -0.14***    -0.07**     -0.04                                       
                            (0.02)      (0.03)      (0.03)                                       

DemocracyBinary6                                                 -0.35                           
                                                                (0.53)                           

DemocracyBinary10                                                            -0.37               
                                                                            (0.46)               

DemocracyL^2                                                                             -0.00   
                                                                                        (0.00)   

PolDis                                                1.00***     1.05***     0.64***     0.97***
                                                    (0.18)      (0.18)      (0.11)      (0.19)   

Relative capability          -0.28***    -0.33***    -0.34***    -0.35***    -0.25***    -0.34***
                            (0.07)      (0.07)      (0.07)      (0.07)      (0.05)      (0.07)   

Major power                   1.71***     1.98***     2.03***     2.03***     2.02***     2.03***
                            (0.31)      (0.29)      (0.28)      (0.28)      (0.20)      (0.28)   

Contiguity                    2.34***     2.30***     2.30***     2.31***     2.79***     2.29***
                            (0.27)      (0.27)      (0.25)      (0.25)      (0.21)      (0.25)   

Distance                     -0.63***    -0.68***    -0.75***    -0.75***    -0.49***    -0.75***
                            (0.10)      (0.11)      (0.09)      (0.09)      (0.08)      (0.09)   

numstate                      0.00       -0.00       -0.00       -0.00        0.00*      -0.00   
                            (0.00)      (0.00)      (0.00)      (0.00)      (0.00)      (0.00)   

Time since last mzfmidl      -0.29***    -0.27***    -0.26***    -0.26***                -0.26***
                            (0.05)      (0.05)      (0.05)      (0.05)                  (0.05)   

(fpceyrs-k1) cubed           -0.00***    -0.00***    -0.00**     -0.00**                 -0.00** 
                            (0.00)      (0.00)      (0.00)      (0.00)                  (0.00)   

(fpceyrs-k2) cubed            0.00**      0.00***     0.00**      0.00**                  0.00** 
                            (0.00)      (0.00)      (0.00)      (0.00)                  (0.00)   

(fpceyrs-k3) cubed           -0.00***    -0.00***    -0.00***    -0.00***                -0.00***
                            (0.00)      (0.00)      (0.00)      (0.00)                  (0.00)   

Time since last mzmidl                                                       -0.39***            
                                                                            (0.03)               

(apceyrs-k1) cubed                                                           -0.00***            
                                                                            (0.00)               

(apceyrs-k2) cubed                                                            0.00***            
                                                                            (0.00)               

(apceyrs-k3) cubed                                                           -0.00*              
                                                                            (0.00)               

Constant                     -1.52       -0.17       -0.24        0.18       -1.60*       0.16   
                            (0.95)      (1.01)      (0.90)      (0.95)      (0.75)      (0.94)   
-------------------------------------------------------------------------------------------------
Observations                321309      321309      300830      300830      300840      300830   
Pseudo R-squared             0.275       0.290       0.297       0.296       0.403       0.297   
Log lik.                  -1775.18    -1737.27    -1628.60    -1630.91    -3767.91    -1628.54   
-------------------------------------------------------------------------------------------------
Standard errors in parentheses
t p<0.10, * p<0.05, ** p<0.01, *** p<0.001

. 
. 
end of do-file

. 
. 
. 
. ***Analysis***
. clear

. cd "$filetree"
/Users/Allan/Dropbox/!!Papers/Liberal Peace/12-02-21_ISQ_commentary/DOR_ISQ_2013_Replication/M_Rep

. **Running robustness specifications on MM data.
. do "12-09-23_looprobustness.do"

. *12-09-23_looprobustness.do
. 
. 
. *Running single imputation robustness code in a loop
. 
. 
. clear

. macro drop _all

. global filetree /Users/Allan/Dropbox/!!Papers/Liberal Peace/12-02-21_ISQ_commentary/DOR_ISQ_2013_Replication/M_R
> ep

. 
. cd "$filetree"
/Users/Allan/Dropbox/!!Papers/Liberal Peace/12-02-21_ISQ_commentary/DOR_ISQ_2013_Replication/M_Rep

. 
. set more off

. 
. use "MM_precise.dta", clear

. 
. sort ccode1 ccode2 year

. gen specification="NA"

. gen specnumber=.
(553787 missing values generated)

. gen n=.
(553787 missing values generated)

. gen DmlCoefficient=.
(553787 missing values generated)

. gen DmlSE=.
(553787 missing values generated)

. gen CIElCoefficient=.
(553787 missing values generated)

. gen CIElSE=.
(553787 missing values generated)

. 
. gen Dmlpvalues=.
(553787 missing values generated)

. gen CIElpvalues=.
(553787 missing values generated)

. 
. 
. sort ccode1 ccode2 year

. global controls lncprt mjpw cntg dist numstate 

. 
. global fmcontrols lncprt mjpw cntg dist numstate fpceyrs fspl1 fspl2 fspl3

. 
. global amcontrols lncprt mjpw cntg dist numstate apceyrs aspl1 aspl2 aspl3

. 
. local j=0

. 
. **M2 with 4 misspecified dependent variables
. local j=`j'+1

. local spec`j' "(Mousseau 2013) Base Model"

. local covars`j' "mzfmidl  CIEl  dml $fmcontrols"

. 
. 
. local j=`j'+1

. local spec`j' "M13 MID Onset"

. local covars`j' "mzamidl CIEl  dml       $amcontrols"

. 
. 
. local j=`j'+1

. local spec`j' "M13 FMID Ongoing"

. local covars`j' "mzfmidongl CIEl  dml                                      $fmcontrols"

. 
. 
. local j=`j'+1

. local spec`j' "M13 MID Ongoing"

. local covars`j' "mzongol CIEl  dml                                         $controls midyears*"

. 
. 
. 
. 
. **M2 with 4 dependent variables x interaction = 8 models
. local j=`j'+1

. local spec`j' ""

. local covars`j' "mzmidonl CIEl  dml                                        $controls midyears*"

. 
. 
. local j=`j'+1

. local spec`j' "O"

. local covars`j' "mzmidol CIEl  dml                                         $controls midyears*"

. 
. 
. local j=`j'+1

. local spec`j' "F"

. local covars`j' "mzfmidonl CIEl  dml                                       $controls fatalyears*"

. 
. 
. local j=`j'+1

. local spec`j' "FO"

. local covars`j'  "mzfmidol CIEl  dml                                       $controls fatalyears*"

. 
. 
. local j=`j'+1

. local spec`j'  "I"

. local covars`j'  "mzmidonl CIElc  dml dmlCIElc                                     $controls midyears*"

. 
. 
. local j=`j'+1

. local spec`j'  "OI"

. local covars`j'  "mzmidol CIElc  dml     dmlCIElc                                  $controls midyears*"

. 
. 
. local j=`j'+1

. local spec`j'  "FI"

. local covars`j'  "mzfmidonl CIElc  dml           dmlCIElc                          $controls fatalyears*"

. 
. 
. local j=`j'+1

. local spec`j'  "FOI"

. local covars`j' "mzfmidol CIElc  dml    dmlCIElc                                   $controls fatalyears*"

. 
. 
. 
. **M2 with DemocracyHigh, with 4 dependent variables x interaction = 8 models
. local j=`j'+1

. local spec`j' "D"

. local covars`j' "mzmidonl CIEl  dml  dmh                                           $controls midyears*"

. 
.  
. local j=`j'+1

. local spec`j'  "OD"

. local covars`j'  "mzmidol CIEl  dml  dmh                                           $controls midyears*"

. 
. 
. local j=`j'+1

. local spec`j'  "FD"

. local covars`j'  "mzfmidonl CIEl  dml  dmh                                         $controls fatalyears*"

. 
. local j=`j'+1

. local spec`j'  "FOD"

. local covars`j'  "mzfmidol CIEl  dml  dmh                                          $controls fatalyears*"

. 
. 
. local j=`j'+1

. local spec`j'  "ID"

. local covars`j'  "mzmidonl CIElc  dml dmlCIElc   dmh                               $controls midyears*"

. 
. 
. local j=`j'+1

. local spec`j'  "OID"

. local covars`j'  "mzmidol CIElc  dml     dmlCIElc        dmh                       $controls midyears*"

. 
. 
. local j=`j'+1

. local spec`j'  "FID"

. local covars`j'  "mzfmidonl CIElc  dml           dmlCIElc        dmh               $controls fatalyears*"

. 
. 
. local j=`j'+1

. local spec`j'  "FOID"

. local covars`j'  "mzfmidol CIElc  dml   dmlCIElc         dmh                       $controls fatalyears*"

. 
. 
. 
. 
. **Estimating Models, Saving Values
. forvalues k=1(1)`j' {
  2. logit `covars`k'', cl(ID) nolog
  3. replace n= e(N)   if _n==`k'
  4. replace specification="`spec`k''" if _n==`k'
  5. replace specnumber=`k' if _n==`k'
  6. replace DmlCoefficient=_b[dml] if _n==`k'
  7. replace DmlSE=_se[dml] if _n==`k'
  8. replace CIElCoefficient=_b[CIEl] if _n==`k'
  9. replace CIElSE=_se[CIEl] if _n==`k'
 10. }

Logistic regression                               Number of obs   =     321568
                                                  Wald chi2(11)   =    1001.66
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -1009.7535                 Pseudo R2       =     0.3950

                                 (Std. Err. adjusted for 12469 clusters in ID)
------------------------------------------------------------------------------
             |               Robust
     mzfmidl |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        CIEl |  -.8023461   .1371956    -5.85   0.000    -1.071245   -.5334476
         dml |   .0024645   .0263217     0.09   0.925     -.049125    .0540541
      lncprt |  -.2827223   .0810421    -3.49   0.000    -.4415619   -.1238826
        mjpw |   1.729984   .3628764     4.77   0.000     1.018759    2.441208
        cntg |   3.770993   .4440998     8.49   0.000     2.900573    4.641412
        dist |  -.5028757   .1197133    -4.20   0.000    -.7375094   -.2682421
    numstate |  -.0040314   .0032147    -1.25   0.210    -.0103322    .0022693
     fpceyrs |   -.269178   .0472021    -5.70   0.000    -.3616924   -.1766635
       fspl1 |  -.0013916   .0006567    -2.12   0.034    -.0026787   -.0001045
       fspl2 |    .000864   .0005676     1.52   0.128    -.0002485    .0019764
       fspl3 |  -.0002656   .0002267    -1.17   0.241    -.0007099    .0001787
       _cons |  -1.711155   1.243062    -1.38   0.169    -4.147513    .7252018
------------------------------------------------------------------------------
(1 real change made)
specification was str2 now str26
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)

Logistic regression                               Number of obs   =     321811
                                                  Wald chi2(11)   =    2649.72
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -4126.5447                 Pseudo R2       =     0.3870

                                 (Std. Err. adjusted for 12469 clusters in ID)
------------------------------------------------------------------------------
             |               Robust
     mzamidl |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        CIEl |  -.2926893   .0561703    -5.21   0.000     -.402781   -.1825975
         dml |  -.0229821   .0133067    -1.73   0.084    -.0490627    .0030985
      lncprt |  -.2293495   .0475264    -4.83   0.000    -.3224996   -.1361994
        mjpw |    1.94587   .1994237     9.76   0.000     1.555007    2.336734
        cntg |    2.81147   .2197577    12.79   0.000     2.380753    3.242188
        dist |  -.3987641   .0791451    -5.04   0.000    -.5538857   -.2436425
    numstate |   .0036793   .0019289     1.91   0.056    -.0001013    .0074599
     apceyrs |  -.3413413   .0282471   -12.08   0.000    -.3967046   -.2859779
       aspl1 |  -.0018696   .0003708    -5.04   0.000    -.0025963   -.0011428
       aspl2 |   .0008769   .0002772     3.16   0.002     .0003337    .0014201
       aspl3 |  -.0001884   .0001127    -1.67   0.094    -.0004092    .0000324
       _cons |  -1.976492   .7189907    -2.75   0.006    -3.385688   -.5672963
------------------------------------------------------------------------------
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)

Logistic regression                               Number of obs   =     321568
                                                  Wald chi2(11)   =     700.29
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -1598.4229                 Pseudo R2       =     0.3242

                                 (Std. Err. adjusted for 12469 clusters in ID)
------------------------------------------------------------------------------
             |               Robust
  mzfmidongl |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        CIEl |  -1.239596   .2315699    -5.35   0.000    -1.693465   -.7857272
         dml |  -.0487297   .0338318    -1.44   0.150    -.1150388    .0175795
      lncprt |  -.3699132   .0983364    -3.76   0.000    -.5626491   -.1771774
        mjpw |   2.357223   .3752041     6.28   0.000     1.621836     3.09261
        cntg |   1.651982   .3240002     5.10   0.000     1.016954    2.287011
        dist |  -.7371872   .1276371    -5.78   0.000    -.9873514    -.487023
    numstate |   .0046404   .0039622     1.17   0.242    -.0031255    .0124062
     fpceyrs |  -.5700711   .0658195    -8.66   0.000    -.6990749   -.4410672
       fspl1 |  -.0057653   .0008927    -6.46   0.000    -.0075149   -.0040157
       fspl2 |   .0042655   .0007204     5.92   0.000     .0028535    .0056775
       fspl3 |  -.0013266    .000266    -4.99   0.000    -.0018479   -.0008052
       _cons |    .816967   1.244107     0.66   0.511    -1.621439    3.255373
------------------------------------------------------------------------------
Note: 62 failures and 0 successes completely determined.
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)

Logistic regression                               Number of obs   =     321811
                                                  Wald chi2(11)   =    1628.76
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -2024.1859                 Pseudo R2       =     0.5314

                                 (Std. Err. adjusted for 12469 clusters in ID)
------------------------------------------------------------------------------
             |               Robust
     mzongol |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        CIEl |  -.7155344   .1317346    -5.43   0.000    -.9737296   -.4573393
         dml |  -.0736326    .024174    -3.05   0.002    -.1210127   -.0262526
      lncprt |   -.271595   .0702455    -3.87   0.000    -.4092736   -.1339164
        mjpw |   2.103181   .2641378     7.96   0.000      1.58548    2.620882
        cntg |   .7961369    .254895     3.12   0.002     .2965519    1.295722
        dist |  -.5855329   .1246529    -4.70   0.000    -.8298481   -.3412177
    numstate |   .0085772   .0028439     3.02   0.003     .0030033     .014151
    midyears |  -1.529749   .0972892   -15.72   0.000    -1.720433   -1.339066
   midyears2 |  -.0147556   .0011841   -12.46   0.000    -.0170764   -.0124347
   midyears3 |   .0083855   .0008106    10.34   0.000     .0067968    .0099743
   midyears4 |  -.0019512   .0003102    -6.29   0.000    -.0025592   -.0013432
       _cons |   .3134704   1.138588     0.28   0.783     -1.91812    2.545061
------------------------------------------------------------------------------
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)

Logistic regression                               Number of obs   =     321316
                                                  Wald chi2(11)   =    2584.95
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -3973.3622                 Pseudo R2       =     0.4096

                                 (Std. Err. adjusted for 12467 clusters in ID)
------------------------------------------------------------------------------
             |               Robust
    mzmidonl |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        CIEl |  -.2875943   .0561976    -5.12   0.000    -.3977395    -.177449
         dml |  -.0252957    .013369    -1.89   0.058    -.0514984    .0009071
      lncprt |  -.2312351   .0476507    -4.85   0.000    -.3246287   -.1378414
        mjpw |   1.941543    .200535     9.68   0.000     1.548502    2.334585
        cntg |   2.773065   .2238023    12.39   0.000      2.33442    3.211709
        dist |    -.41828   .0860719    -4.86   0.000    -.5869778   -.2495822
    numstate |   .0043584   .0019377     2.25   0.024     .0005605    .0081562
    midyears |  -.4754962   .0347933   -13.67   0.000    -.5436898   -.4073025
   midyears2 |  -.0034161     .00043    -7.94   0.000    -.0042588   -.0025733
   midyears3 |   .0018021   .0003064     5.88   0.000     .0012015    .0024027
   midyears4 |   -.000413   .0001188    -3.48   0.001    -.0006459   -.0001801
       _cons |  -1.644376   .7800609    -2.11   0.035    -3.173267   -.1154843
------------------------------------------------------------------------------
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)

Logistic regression                               Number of obs   =     321811
                                                  Wald chi2(11)   =    2638.84
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -5156.7215                 Pseudo R2       =     0.4563

                                 (Std. Err. adjusted for 12469 clusters in ID)
------------------------------------------------------------------------------
             |               Robust
     mzmidol |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        CIEl |  -.3577637   .0604579    -5.92   0.000    -.4762589   -.2392684
         dml |  -.0448444   .0135947    -3.30   0.001    -.0714895   -.0181992
      lncprt |  -.2497392    .047463    -5.26   0.000    -.3427649   -.1567136
        mjpw |   2.179802   .1957139    11.14   0.000      1.79621    2.563394
        cntg |   2.132457   .1971978    10.81   0.000     1.745957    2.518958
        dist |  -.5221786   .0916779    -5.70   0.000     -.701864   -.3424932
    numstate |   .0065315   .0018877     3.46   0.001     .0028316    .0102313
    midyears |  -.6867033   .0354714   -19.36   0.000     -.756226   -.6171806
   midyears2 |  -.0056181   .0004216   -13.33   0.000    -.0064444   -.0047918
   midyears3 |   .0031156   .0002914    10.69   0.000     .0025445    .0036867
   midyears4 |  -.0007484   .0001091    -6.86   0.000    -.0009623   -.0005346
       _cons |  -.1360753   .7901031    -0.17   0.863    -1.684649    1.412498
------------------------------------------------------------------------------
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)

Logistic regression                               Number of obs   =     321309
                                                  Wald chi2(11)   =    1193.00
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -1727.9961                 Pseudo R2       =     0.2943

                                 (Std. Err. adjusted for 12467 clusters in ID)
------------------------------------------------------------------------------
             |               Robust
   mzfmidonl |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        CIEl |  -.6542419   .0978092    -6.69   0.000    -.8459445   -.4625394
         dml |  -.0706433   .0253834    -2.78   0.005    -.1203938   -.0208928
      lncprt |  -.3370001   .0692731    -4.86   0.000    -.4727729   -.2012274
        mjpw |   1.959844   .2912358     6.73   0.000     1.389032    2.530655
        cntg |   2.324271   .2846292     8.17   0.000     1.766409    2.882134
        dist |  -.6269455   .1144006    -5.48   0.000    -.8511665   -.4027245
    numstate |  -.0014272   .0026351    -0.54   0.588     -.006592    .0037376
  fatalyears |  -.2877555   .0564586    -5.10   0.000    -.3984123   -.1770987
 fatalyears2 |  -.0020178   .0006138    -3.29   0.001    -.0032209   -.0008146
 fatalyears3 |   .0014896   .0004642     3.21   0.001     .0005799    .0023994
 fatalyears4 |  -.0005612   .0001612    -3.48   0.000    -.0008772   -.0002452
       _cons |  -.4387213   1.050133    -0.42   0.676    -2.496944    1.619502
------------------------------------------------------------------------------
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)

Logistic regression                               Number of obs   =     321568
                                                  Wald chi2(11)   =    1722.31
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -2409.2424                 Pseudo R2       =     0.4211

                                 (Std. Err. adjusted for 12469 clusters in ID)
------------------------------------------------------------------------------
             |               Robust
    mzfmidol |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        CIEl |   -.736509     .10335    -7.13   0.000    -.9390713   -.5339467
         dml |  -.0773998   .0261083    -2.96   0.003     -.128571   -.0262285
      lncprt |  -.3211645   .0704626    -4.56   0.000    -.4592686   -.1830604
        mjpw |   2.219247   .3088877     7.18   0.000     1.613838    2.824656
        cntg |   1.859016    .259445     7.17   0.000     1.350513    2.367519
        dist |  -.6480776   .0966545    -6.71   0.000     -.837517   -.4586382
    numstate |   .0064083   .0025939     2.47   0.013     .0013243    .0114922
  fatalyears |  -.7867016   .0473609   -16.61   0.000    -.8795272    -.693876
 fatalyears2 |  -.0070694   .0005581   -12.67   0.000    -.0081632   -.0059756
 fatalyears3 |   .0047879   .0004341    11.03   0.000     .0039371    .0056387
 fatalyears4 |  -.0012777   .0001598    -7.99   0.000     -.001591   -.0009645
       _cons |   .5333328   .9425538     0.57   0.572    -1.314039    2.380704
------------------------------------------------------------------------------
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)

Logistic regression                               Number of obs   =     321316
                                                  Wald chi2(12)   =    2585.09
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -3963.0964                 Pseudo R2       =     0.4112

                                 (Std. Err. adjusted for 12467 clusters in ID)
------------------------------------------------------------------------------
             |               Robust
    mzmidonl |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       CIElc |  -.2583981   .0597578    -4.32   0.000    -.3755213    -.141275
         dml |  -.0301065   .0119339    -2.52   0.012    -.0534965   -.0067166
    dmlCIElc |  -.0200595   .0072587    -2.76   0.006    -.0342863   -.0058326
      lncprt |  -.2370792    .046471    -5.10   0.000    -.3281607   -.1459977
        mjpw |   1.989839   .1926459    10.33   0.000      1.61226    2.367418
        cntg |   2.766233   .2162682    12.79   0.000     2.342355    3.190111
        dist |  -.4367239   .0820175    -5.32   0.000    -.5974752   -.2759726
    numstate |   .0040278   .0019521     2.06   0.039     .0002018    .0078538
    midyears |  -.4750467   .0346141   -13.72   0.000    -.5428892   -.4072042
   midyears2 |  -.0034194   .0004286    -7.98   0.000    -.0042595   -.0025793
   midyears3 |   .0018165   .0003058     5.94   0.000     .0012171     .002416
   midyears4 |  -.0004262   .0001187    -3.59   0.000    -.0006589   -.0001935
       _cons |   -1.78948   .7517877    -2.38   0.017    -3.262957   -.3160029
------------------------------------------------------------------------------
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)

Logistic regression                               Number of obs   =     321811
                                                  Wald chi2(12)   =    2646.65
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -5155.3758                 Pseudo R2       =     0.4565

                                 (Std. Err. adjusted for 12469 clusters in ID)
------------------------------------------------------------------------------
             |               Robust
     mzmidol |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       CIElc |   -.353893   .0623765    -5.67   0.000    -.4761487   -.2316372
         dml |  -.0469296   .0129707    -3.62   0.000    -.0723516   -.0215075
    dmlCIElc |  -.0067853   .0071697    -0.95   0.344    -.0208377     .007267
      lncprt |  -.2513718   .0470353    -5.34   0.000    -.3435593   -.1591844
        mjpw |   2.191659   .1908919    11.48   0.000     1.817518      2.5658
        cntg |   2.131695   .1953442    10.91   0.000     1.748828    2.514563
        dist |  -.5264764   .0904246    -5.82   0.000    -.7037053   -.3492474
    numstate |   .0064221   .0018996     3.38   0.001      .002699    .0101451
    midyears |  -.6865856   .0354704   -19.36   0.000    -.7561062    -.617065
   midyears2 |  -.0056197   .0004211   -13.35   0.000    -.0064451   -.0047944
   midyears3 |   .0031206   .0002906    10.74   0.000      .002551    .0036903
   midyears4 |  -.0007527   .0001087    -6.92   0.000    -.0009658   -.0005396
       _cons |  -.6017508   .7626946    -0.79   0.430    -2.096605    .8931032
------------------------------------------------------------------------------
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)

Logistic regression                               Number of obs   =     321309
                                                  Wald chi2(12)   =    1184.01
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -1719.9074                 Pseudo R2       =     0.2976

                                 (Std. Err. adjusted for 12467 clusters in ID)
------------------------------------------------------------------------------
             |               Robust
   mzfmidonl |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       CIElc |  -.9948726   .1345056    -7.40   0.000    -1.258499   -.7312466
         dml |  -.1144615   .0262951    -4.35   0.000    -.1659989   -.0629241
    dmlCIElc |  -.0702426   .0155728    -4.51   0.000    -.1007647   -.0397206
      lncprt |  -.3422634   .0687408    -4.98   0.000    -.4769929   -.2075339
        mjpw |   2.014663   .2805677     7.18   0.000     1.464761    2.564566
        cntg |   2.342984    .278208     8.42   0.000     1.797707    2.888262
        dist |  -.6371531   .1101466    -5.78   0.000    -.8530365   -.4212698
    numstate |  -.0014804   .0026016    -0.57   0.569    -.0065794    .0036187
  fatalyears |  -.2876797   .0563954    -5.10   0.000    -.3982126   -.1771468
 fatalyears2 |  -.0020169   .0006135    -3.29   0.001    -.0032193   -.0008145
 fatalyears3 |   .0015085   .0004625     3.26   0.001      .000602    .0024151
 fatalyears4 |   -.000581     .00016    -3.63   0.000    -.0008946   -.0002674
       _cons |  -1.496685   1.026628    -1.46   0.145    -3.508839    .5154678
------------------------------------------------------------------------------
Note: 1020 failures and 0 successes completely determined.
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)

Logistic regression                               Number of obs   =     321568
                                                  Wald chi2(12)   =    1724.87
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood =  -2406.333                 Pseudo R2       =     0.4218

                                 (Std. Err. adjusted for 12469 clusters in ID)
------------------------------------------------------------------------------
             |               Robust
    mzfmidol |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       CIElc |  -.8693643   .1134472    -7.66   0.000    -1.091717   -.6470119
         dml |  -.0976129   .0288194    -3.39   0.001     -.154098   -.0411279
    dmlCIElc |  -.0309942    .015488    -2.00   0.045    -.0613502   -.0006382
      lncprt |  -.3225002   .0703999    -4.58   0.000    -.4604815   -.1845189
        mjpw |   2.239577   .3034044     7.38   0.000     1.644915    2.834238
        cntg |   1.872629   .2592635     7.22   0.000     1.364482    2.380776
        dist |  -.6501945   .0949949    -6.84   0.000     -.836381    -.464008
    numstate |   .0063355   .0025672     2.47   0.014     .0013039    .0113671
  fatalyears |  -.7859921   .0475458   -16.53   0.000    -.8791802   -.6928041
 fatalyears2 |  -.0070623   .0005605   -12.60   0.000    -.0081608   -.0059637
 fatalyears3 |   .0047918   .0004335    11.05   0.000     .0039421    .0056415
 fatalyears4 |  -.0012855   .0001584    -8.11   0.000     -.001596    -.000975
       _cons |  -.6149687   .9073002    -0.68   0.498    -2.393244    1.163307
------------------------------------------------------------------------------
Note: 15 failures and 0 successes completely determined.
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)

Logistic regression                               Number of obs   =     321316
                                                  Wald chi2(12)   =    2527.14
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -3940.6469                 Pseudo R2       =     0.4145

                                 (Std. Err. adjusted for 12467 clusters in ID)
------------------------------------------------------------------------------
             |               Robust
    mzmidonl |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        CIEl |   -.333321   .0600721    -5.55   0.000    -.4510602   -.2155818
         dml |  -.0459677   .0124013    -3.71   0.000    -.0702737   -.0216617
         dmh |   .0477675   .0090739     5.26   0.000      .029983    .0655519
      lncprt |  -.2348342   .0460287    -5.10   0.000    -.3250487   -.1446197
        mjpw |   1.869198   .1887511     9.90   0.000     1.499253    2.239144
        cntg |   2.888198   .2121735    13.61   0.000     2.472345     3.30405
        dist |  -.4320598   .0784001    -5.51   0.000    -.5857212   -.2783983
    numstate |   .0040512   .0018798     2.16   0.031     .0003668    .0077356
    midyears |  -.4679958   .0343185   -13.64   0.000    -.5352588   -.4007328
   midyears2 |  -.0034084   .0004286    -7.95   0.000    -.0042484   -.0025684
   midyears3 |   .0018226   .0003063     5.95   0.000     .0012223    .0024229
   midyears4 |  -.0004295    .000119    -3.61   0.000    -.0006628   -.0001962
       _cons |  -1.784665   .7236336    -2.47   0.014    -3.202961   -.3663696
------------------------------------------------------------------------------
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)

Logistic regression                               Number of obs   =     321811
                                                  Wald chi2(12)   =    2663.15
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -5107.3646                 Pseudo R2       =     0.4615

                                 (Std. Err. adjusted for 12469 clusters in ID)
------------------------------------------------------------------------------
             |               Robust
     mzmidol |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        CIEl |   -.412815    .065682    -6.29   0.000    -.5415493   -.2840807
         dml |  -.0658506   .0126747    -5.20   0.000    -.0906925   -.0410086
         dmh |   .0511727   .0092668     5.52   0.000     .0330102    .0693352
      lncprt |  -.2509308   .0457725    -5.48   0.000    -.3406432   -.1612183
        mjpw |   2.066831   .1786148    11.57   0.000     1.716752     2.41691
        cntg |   2.282935   .1918616    11.90   0.000     1.906894    2.658977
        dist |  -.5284746   .0851275    -6.21   0.000    -.6953215   -.3616278
    numstate |   .0059647   .0018409     3.24   0.001     .0023566    .0095728
    midyears |   -.677123   .0349764   -19.36   0.000    -.7456756   -.6085705
   midyears2 |  -.0055889   .0004178   -13.38   0.000    -.0064078   -.0047701
   midyears3 |   .0031217   .0002895    10.78   0.000     .0025544     .003689
   midyears4 |  -.0007598   .0001088    -6.98   0.000    -.0009731   -.0005466
       _cons |  -.3121801   .7530833    -0.41   0.678    -1.788196    1.163836
------------------------------------------------------------------------------
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)

Logistic regression                               Number of obs   =     321309
                                                  Wald chi2(12)   =    1229.18
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -1708.5374                 Pseudo R2       =     0.3022

                                 (Std. Err. adjusted for 12467 clusters in ID)
------------------------------------------------------------------------------
             |               Robust
   mzfmidonl |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        CIEl |  -.7344062   .0965477    -7.61   0.000    -.9236363   -.5451762
         dml |  -.0971484   .0235637    -4.12   0.000    -.1433324   -.0509644
         dmh |   .0588872   .0134786     4.37   0.000     .0324697    .0853047
      lncprt |   -.334106    .066904    -4.99   0.000    -.4652354   -.2029766
        mjpw |   1.838062   .2815172     6.53   0.000     1.286299    2.389826
        cntg |   2.478535   .2716581     9.12   0.000     1.946095    3.010975
        dist |  -.6331537   .1001471    -6.32   0.000    -.8294385    -.436869
    numstate |  -.0017276   .0026161    -0.66   0.509     -.006855    .0033998
  fatalyears |  -.2706173   .0570118    -4.75   0.000    -.3823584   -.1588762
 fatalyears2 |  -.0019275   .0006212    -3.10   0.002     -.003145     -.00071
 fatalyears3 |   .0014633   .0004689     3.12   0.002     .0005443    .0023823
 fatalyears4 |  -.0005702   .0001625    -3.51   0.000    -.0008886   -.0002518
       _cons |  -.7218719   .9407691    -0.77   0.443    -2.565745    1.122002
------------------------------------------------------------------------------
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)

Logistic regression                               Number of obs   =     321568
                                                  Wald chi2(12)   =    1819.91
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -2376.4827                 Pseudo R2       =     0.4290

                                 (Std. Err. adjusted for 12469 clusters in ID)
------------------------------------------------------------------------------
             |               Robust
    mzfmidol |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        CIEl |  -.8339481   .1041474    -8.01   0.000    -1.038073   -.6298229
         dml |  -.1051351   .0252291    -4.17   0.000    -.1545832    -.055687
         dmh |   .0628895   .0137927     4.56   0.000     .0358564    .0899227
      lncprt |  -.3245633   .0687005    -4.72   0.000    -.4592138   -.1899128
        mjpw |   2.067168   .2769214     7.46   0.000     1.524412    2.609924
        cntg |    2.06835   .2628345     7.87   0.000     1.553204    2.583496
        dist |  -.6340586   .0881012    -7.20   0.000    -.8067339   -.4613834
    numstate |   .0058653   .0026316     2.23   0.026     .0007073    .0110232
  fatalyears |  -.7715293   .0471439   -16.37   0.000    -.8639297   -.6791289
 fatalyears2 |  -.0070002   .0005536   -12.64   0.000    -.0080853   -.0059151
 fatalyears3 |   .0047751   .0004309    11.08   0.000     .0039306    .0056197
 fatalyears4 |  -.0012894   .0001599    -8.06   0.000    -.0016027    -.000976
       _cons |   .1313192   .9287448     0.14   0.888    -1.688987    1.951625
------------------------------------------------------------------------------
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)

Logistic regression                               Number of obs   =     321316
                                                  Wald chi2(13)   =    2536.45
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -3937.0509                 Pseudo R2       =     0.4150

                                 (Std. Err. adjusted for 12467 clusters in ID)
------------------------------------------------------------------------------
             |               Robust
    mzmidonl |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       CIElc |  -.3155116   .0641605    -4.92   0.000     -.441264   -.1897593
         dml |  -.0472124   .0117448    -4.02   0.000    -.0702317    -.024193
    dmlCIElc |  -.0122895   .0074396    -1.65   0.099    -.0268707    .0022918
         dmh |   .0439819   .0095071     4.63   0.000     .0253484    .0626155
      lncprt |   -.238198   .0454894    -5.24   0.000    -.3273556   -.1490404
        mjpw |   1.904614   .1832807    10.39   0.000     1.545391    2.263838
        cntg |   2.873418   .2084154    13.79   0.000     2.464932    3.281905
        dist |  -.4420139   .0771321    -5.73   0.000      -.59319   -.2908379
    numstate |   .0038655   .0018989     2.04   0.042     .0001437    .0075874
    midyears |  -.4683553   .0342113   -13.69   0.000    -.5354082   -.4013024
   midyears2 |  -.0034151   .0004278    -7.98   0.000    -.0042536   -.0025765
   midyears3 |   .0018329   .0003059     5.99   0.000     .0012333    .0024325
   midyears4 |  -.0004372   .0001189    -3.68   0.000    -.0006703   -.0002041
       _cons |   -2.09936   .7195908    -2.92   0.004    -3.509732   -.6889884
------------------------------------------------------------------------------
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)

Logistic regression                               Number of obs   =     321811
                                                  Wald chi2(13)   =    2667.29
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -5107.2882                 Pseudo R2       =     0.4615

                                 (Std. Err. adjusted for 12469 clusters in ID)
------------------------------------------------------------------------------
             |               Robust
     mzmidol |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       CIElc |  -.4136139   .0664326    -6.23   0.000    -.5438194   -.2834084
         dml |  -.0654999   .0126157    -5.19   0.000    -.0902263   -.0407736
    dmlCIElc |   .0016688   .0073912     0.23   0.821    -.0128177    .0161553
         dmh |   .0515781   .0097161     5.31   0.000      .032535    .0706213
      lncprt |  -.2505393   .0455533    -5.50   0.000    -.3398222   -.1612565
        mjpw |   2.062956   .1744083    11.83   0.000     1.721122     2.40479
        cntg |   2.284468     .19139    11.94   0.000      1.90935    2.659585
        dist |  -.5275346   .0851144    -6.20   0.000    -.6943557   -.3607135
    numstate |   .0059864   .0018455     3.24   0.001     .0023692    .0096036
    midyears |  -.6770496   .0349389   -19.38   0.000    -.7455286   -.6085707
   midyears2 |  -.0055876   .0004172   -13.39   0.000    -.0064052     -.00477
   midyears3 |     .00312   .0002888    10.80   0.000      .002554     .003686
   midyears4 |  -.0007587   .0001085    -6.99   0.000    -.0009715    -.000546
       _cons |  -.9653075    .742946    -1.30   0.194    -2.421455    .4908398
------------------------------------------------------------------------------
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)

Logistic regression                               Number of obs   =     321309
                                                  Wald chi2(13)   =    1205.24
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -1703.1251                 Pseudo R2       =     0.3044

                                 (Std. Err. adjusted for 12467 clusters in ID)
------------------------------------------------------------------------------
             |               Robust
   mzfmidonl |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       CIElc |  -1.033411   .1319211    -7.83   0.000    -1.291972   -.7748507
         dml |  -.1335509   .0253428    -5.27   0.000    -.1832219   -.0838798
    dmlCIElc |     -.0594     .01572    -3.78   0.000    -.0902108   -.0285893
         dmh |   .0553998    .013437     4.12   0.000     .0290638    .0817358
      lncprt |  -.3393376   .0666948    -5.09   0.000    -.4700569   -.2086183
        mjpw |   1.888217    .273036     6.92   0.000     1.353077    2.423358
        cntg |   2.486475   .2669046     9.32   0.000     1.963352    3.009599
        dist |  -.6395977   .0983944    -6.50   0.000    -.8324471   -.4467483
    numstate |  -.0017523    .002587    -0.68   0.498    -.0068227    .0033181
  fatalyears |  -.2713041   .0569184    -4.77   0.000     -.382862   -.1597461
 fatalyears2 |  -.0019346   .0006202    -3.12   0.002    -.0031501    -.000719
 fatalyears3 |   .0014827   .0004669     3.18   0.001     .0005676    .0023978
 fatalyears4 |  -.0005866   .0001613    -3.64   0.000    -.0009028   -.0002705
       _cons |  -1.907254   .9349393    -2.04   0.041    -3.739701   -.0748063
------------------------------------------------------------------------------
Note: 840 failures and 0 successes completely determined.
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)

Logistic regression                               Number of obs   =     321568
                                                  Wald chi2(13)   =    1835.81
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -2375.4809                 Pseudo R2       =     0.4292

                                 (Std. Err. adjusted for 12469 clusters in ID)
------------------------------------------------------------------------------
             |               Robust
    mzfmidol |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       CIElc |  -.9176608   .1081523    -8.48   0.000    -1.129635   -.7056861
         dml |  -.1172061   .0279034    -4.20   0.000    -.1718958   -.0625164
    dmlCIElc |  -.0188122   .0156394    -1.20   0.229    -.0494647    .0118404
         dmh |   .0616232   .0141216     4.36   0.000     .0339454    .0893011
      lncprt |  -.3253213   .0688063    -4.73   0.000    -.4601791   -.1904635
        mjpw |   2.081927    .273155     7.62   0.000     1.546553    2.617301
        cntg |   2.071864   .2617737     7.91   0.000     1.558797     2.58493
        dist |  -.6351874   .0877891    -7.24   0.000    -.8072509    -.463124
    numstate |   .0058402   .0026154     2.23   0.026      .000714    .0109663
  fatalyears |  -.7714393   .0472211   -16.34   0.000     -.863991   -.6788877
 fatalyears2 |  -.0069993   .0005546   -12.62   0.000    -.0080863   -.0059123
 fatalyears3 |   .0047792   .0004298    11.12   0.000     .0039368    .0056216
 fatalyears4 |  -.0012941   .0001586    -8.16   0.000    -.0016049   -.0009833
       _cons |  -1.158799   .9056471    -1.28   0.201    -2.933834     .616237
------------------------------------------------------------------------------
Note: 8 failures and 0 successes completely determined.
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)

.  
. 
. replace Dmlpvalues=2*normal(-abs(DmlCoefficient/DmlSE))
(20 real changes made)

. replace CIElpvalues=2*normal(-abs(CIElCoefficient/CIElSE))
(20 real changes made)

. keep specification-CIElpvalues

. drop if specnumber==.
(553767 observations deleted)

. saveold "robustness1.dta", replace
(note: file robustness1.dta not found)
file robustness1.dta saved

. 
. 
. 
. 
. 
. **Analyses with no ln(life insurance) or GDP
. 
. clear

. macro drop _all

. global filetree /Users/Allan/Dropbox/!!Papers/Liberal Peace/12-02-21_ISQ_commentary/DOR_ISQ_2013_Replication/M_R
> ep

. 
. cd "$filetree"
/Users/Allan/Dropbox/!!Papers/Liberal Peace/12-02-21_ISQ_commentary/DOR_ISQ_2013_Replication/M_Rep

. 
. set more off

. 
. use "MM_precise.dta", clear

. 
. sort ccode1 ccode2 year

. gen specification="NA"

. gen specnumber=.
(553787 missing values generated)

. gen n=.
(553787 missing values generated)

. gen DmlCoefficient=.
(553787 missing values generated)

. gen DmlSE=.
(553787 missing values generated)

. gen CIElCoefficient=.
(553787 missing values generated)

. gen CIElSE=.
(553787 missing values generated)

. 
. gen Dmlpvalues=.
(553787 missing values generated)

. gen CIElpvalues=.
(553787 missing values generated)

. 
. sort ccode1 ccode2 year

. global controls lncprt mjpw cntg dist numstate 

. 
. global fmcontrols lncprt mjpw cntg dist numstate fpceyrs fspl1 fspl2 fspl3

. 
. global amcontrols lncprt mjpw cntg dist numstate apceyrs aspl1 aspl2 aspl3

. 
. local j=0

. 
. **M1 with 4 dependent variables x DemocracyHigh = 8 models
. local j=`j'+1

. local spec`j' "n"

. local covars`j' "mzmidonl   dml                                            $controls midyears*"

. 
. local j=`j'+1

. local spec`j' "On"

. local covars`j' "mzmidol   dml                                     $controls midyears*"

. 
. local j=`j'+1

. local spec`j' "Fn"

. local covars`j' "mzfmidonl   dml                                           $controls fatalyears*"

. 
. local j=`j'+1

. local spec`j' "FOn"

. local covars`j' "mzfmidol   dml                                            $controls fatalyears*"

. 
. 
. local j=`j'+1

. local spec`j' "Dn"

. local covars`j' "mzmidonl   dml  dmh                                       $controls midyears*"

. 
. local j=`j'+1

. local spec`j' "ODn"

. local covars`j' "mzmidol   dml  dmh                                        $controls midyears*"

. 
. local j=`j'+1

. local spec`j' "FDn"

. local covars`j' "mzfmidonl   dml  dmh                                      $controls fatalyears*"

. 
. local j=`j'+1

. local spec`j' "FODn"

. local covars`j' "mzfmidol   dml  dmh                                       $controls fatalyears*"

. 
. 
. 
. **Estimating Models, Saving Values
. forvalues k=1(1)`j' {
  2. logit `covars`k'', cl(ID) nolog
  3. replace n= e(N)   if _n==`k'
  4. replace specification="`spec`k''" if _n==`k'
  5. replace specnumber=`k' if _n==`k'
  6. replace DmlCoefficient=_b[dml] if _n==`k'
  7. replace DmlSE=_se[dml] if _n==`k'
  8. *replace CIElCoefficient=_b[CIEl] if _n==`k'
. *replace CIElSE=_se[CIEl] if _n==`k'
. }

Logistic regression                               Number of obs   =     391599
                                                  Wald chi2(10)   =    3071.35
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -4847.9516                 Pseudo R2       =     0.3961

                                 (Std. Err. adjusted for 13538 clusters in ID)
------------------------------------------------------------------------------
             |               Robust
    mzmidonl |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         dml |  -.0644432   .0120213    -5.36   0.000    -.0880046   -.0408819
      lncprt |  -.2017229   .0426116    -4.73   0.000    -.2852402   -.1182057
        mjpw |   1.854115   .1899964     9.76   0.000     1.481729    2.226501
        cntg |    2.93306   .2025296    14.48   0.000      2.53611    3.330011
        dist |  -.3866963    .069427    -5.57   0.000    -.5227708   -.2506219
    numstate |   .0061531    .001831     3.36   0.001     .0025643    .0097418
    midyears |   -.453336   .0307835   -14.73   0.000    -.5136704   -.3930015
   midyears2 |  -.0033181   .0003874    -8.56   0.000    -.0040774   -.0025587
   midyears3 |   .0018078   .0002814     6.42   0.000     .0012562    .0023594
   midyears4 |  -.0004465   .0001124    -3.97   0.000    -.0006667   -.0002262
       _cons |  -2.883126   .6353301    -4.54   0.000     -4.12835   -1.637902
------------------------------------------------------------------------------
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)

Logistic regression                               Number of obs   =     392172
                                                  Wald chi2(10)   =    2874.82
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -6359.1072                 Pseudo R2       =     0.4339

                                 (Std. Err. adjusted for 13538 clusters in ID)
------------------------------------------------------------------------------
             |               Robust
     mzmidol |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         dml |  -.0862406   .0128025    -6.74   0.000     -.111333   -.0611481
      lncprt |  -.2280059   .0422478    -5.40   0.000    -.3108102   -.1452017
        mjpw |   2.130202   .1900308    11.21   0.000     1.757749    2.502656
        cntg |   2.342857   .1860919    12.59   0.000     1.978124     2.70759
        dist |  -.4836598   .0765107    -6.32   0.000    -.6336181   -.3337015
    numstate |   .0077349   .0017959     4.31   0.000     .0042149    .0112548
    midyears |  -.6496847   .0317452   -20.47   0.000     -.711904   -.5874653
   midyears2 |  -.0053849    .000381   -14.14   0.000    -.0061316   -.0046383
   midyears3 |   .0030458   .0002675    11.39   0.000     .0025215      .00357
   midyears4 |  -.0007647    .000103    -7.42   0.000    -.0009666   -.0005628
       _cons |  -1.467091   .6597172    -2.22   0.026    -2.760113    -.174069
------------------------------------------------------------------------------
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)

Logistic regression                               Number of obs   =     391550
                                                  Wald chi2(10)   =    1293.67
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -2103.9903                 Pseudo R2       =     0.2722

                                 (Std. Err. adjusted for 13538 clusters in ID)
------------------------------------------------------------------------------
             |               Robust
   mzfmidonl |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         dml |  -.1302167   .0212867    -6.12   0.000     -.171938   -.0884955
      lncprt |  -.3026787   .0596622    -5.07   0.000    -.4196145   -.1857429
        mjpw |   1.837594   .2860187     6.42   0.000     1.277008     2.39818
        cntg |    2.48239   .2645373     9.38   0.000     1.963907    3.000874
        dist |  -.5678079   .0972482    -5.84   0.000    -.7584108    -.377205
    numstate |   .0029911   .0025795     1.16   0.246    -.0020647    .0080468
  fatalyears |  -.3159632   .0477956    -6.61   0.000    -.4096409   -.2222856
 fatalyears2 |  -.0024217   .0005478    -4.42   0.000    -.0034954   -.0013481
 fatalyears3 |   .0017785   .0004241     4.19   0.000     .0009472    .0026098
 fatalyears4 |  -.0006286   .0001511    -4.16   0.000    -.0009247   -.0003324
       _cons |  -2.513566   .9169079    -2.74   0.006    -4.310673   -.7164599
------------------------------------------------------------------------------
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)

Logistic regression                               Number of obs   =     391837
                                                  Wald chi2(10)   =    1651.52
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood =  -2939.249                 Pseudo R2       =     0.3882

                                 (Std. Err. adjusted for 13538 clusters in ID)
------------------------------------------------------------------------------
             |               Robust
    mzfmidol |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         dml |  -.1376065   .0231202    -5.95   0.000    -.1829213   -.0922916
      lncprt |   -.339329   .0625018    -5.43   0.000    -.4618302   -.2168278
        mjpw |   2.183745   .3115052     7.01   0.000     1.573206    2.794284
        cntg |   2.171696   .2567003     8.46   0.000     1.668573    2.674819
        dist |  -.5795624   .0881383    -6.58   0.000    -.7523103   -.4068144
    numstate |   .0083516    .002623     3.18   0.001     .0032106    .0134927
  fatalyears |  -.7534536   .0426384   -17.67   0.000    -.8370233   -.6698838
 fatalyears2 |  -.0068365   .0005261   -12.99   0.000    -.0078678   -.0058053
 fatalyears3 |   .0046588   .0004153    11.22   0.000     .0038448    .0054728
 fatalyears4 |  -.0012546   .0001533    -8.19   0.000     -.001555   -.0009542
       _cons |  -1.509439   .8501534    -1.78   0.076    -3.175709     .156831
------------------------------------------------------------------------------
(1 real change made)
specification was str2 now str3
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)

Logistic regression                               Number of obs   =     391599
                                                  Wald chi2(11)   =    3023.73
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -4814.7999                 Pseudo R2       =     0.4002

                                 (Std. Err. adjusted for 13538 clusters in ID)
------------------------------------------------------------------------------
             |               Robust
    mzmidonl |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         dml |  -.0886939   .0119919    -7.40   0.000    -.1121976   -.0651901
         dmh |   .0427132    .007779     5.49   0.000     .0274666    .0579597
      lncprt |  -.2014842   .0408639    -4.93   0.000     -.281576   -.1213925
        mjpw |    1.75874    .179726     9.79   0.000     1.406484    2.110996
        cntg |   3.040138   .1921961    15.82   0.000      2.66344    3.416835
        dist |  -.3945318   .0635417    -6.21   0.000    -.5190713   -.2699924
    numstate |   .0059007   .0017977     3.28   0.001     .0023771    .0094242
    midyears |  -.4476855   .0303168   -14.77   0.000    -.5071052   -.3882657
   midyears2 |  -.0033055   .0003852    -8.58   0.000    -.0040605   -.0025506
   midyears3 |    .001823   .0002809     6.49   0.000     .0012724    .0023735
   midyears4 |  -.0004619   .0001124    -4.11   0.000    -.0006823   -.0002415
       _cons |  -3.087661   .5997792    -5.15   0.000    -4.263207   -1.912116
------------------------------------------------------------------------------
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)

Logistic regression                               Number of obs   =     392172
                                                  Wald chi2(11)   =    2872.81
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -6312.0528                 Pseudo R2       =     0.4381

                                 (Std. Err. adjusted for 13538 clusters in ID)
------------------------------------------------------------------------------
             |               Robust
     mzmidol |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         dml |  -.1101214   .0126922    -8.68   0.000    -.1349975   -.0852452
         dmh |   .0441794   .0078187     5.65   0.000      .028855    .0595037
      lncprt |   -.227093   .0408746    -5.56   0.000    -.3072057   -.1469803
        mjpw |   2.008961   .1761081    11.41   0.000     1.663795    2.354126
        cntg |   2.474951   .1792604    13.81   0.000     2.123607    2.826295
        dist |  -.4848773   .0706687    -6.86   0.000    -.6233853   -.3463692
    numstate |   .0072108   .0017723     4.07   0.000     .0037372    .0106845
    midyears |  -.6434087   .0313661   -20.51   0.000    -.7048851   -.5819323
   midyears2 |  -.0053633   .0003782   -14.18   0.000    -.0061046    -.004622
   midyears3 |   .0030539   .0002663    11.47   0.000     .0025319    .0035759
   midyears4 |  -.0007774   .0001028    -7.56   0.000    -.0009789   -.0005758
       _cons |   -1.68714   .6295247    -2.68   0.007    -2.920985   -.4532941
------------------------------------------------------------------------------
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)

Logistic regression                               Number of obs   =     391550
                                                  Wald chi2(11)   =    1402.74
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -2087.7274                 Pseudo R2       =     0.2778

                                 (Std. Err. adjusted for 13538 clusters in ID)
------------------------------------------------------------------------------
             |               Robust
   mzfmidonl |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         dml |  -.1566599    .019925    -7.86   0.000    -.1957122   -.1176077
         dmh |   .0484438   .0118658     4.08   0.000     .0251874    .0717003
      lncprt |  -.2983073   .0574769    -5.19   0.000    -.4109598   -.1856547
        mjpw |   1.716671   .2752465     6.24   0.000     1.177198    2.256145
        cntg |   2.619581   .2546367    10.29   0.000     2.120502     3.11866
        dist |  -.5685452   .0862739    -6.59   0.000    -.7376389   -.3994515
    numstate |   .0026195   .0025504     1.03   0.304    -.0023793    .0076183
  fatalyears |  -.3044125   .0476281    -6.39   0.000    -.3977618   -.2110633
 fatalyears2 |  -.0023503   .0005471    -4.30   0.000    -.0034226   -.0012779
 fatalyears3 |   .0017588   .0004244     4.14   0.000     .0009271    .0025906
 fatalyears4 |  -.0006389   .0001517    -4.21   0.000    -.0009362   -.0003416
       _cons |  -2.803074   .8329081    -3.37   0.001    -4.435543   -1.170604
------------------------------------------------------------------------------
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)

Logistic regression                               Number of obs   =     391837
                                                  Wald chi2(11)   =    1793.38
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -2913.2292                 Pseudo R2       =     0.3936

                                 (Std. Err. adjusted for 13538 clusters in ID)
------------------------------------------------------------------------------
             |               Robust
    mzfmidol |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         dml |  -.1642937   .0226126    -7.27   0.000    -.2086135   -.1199738
         dmh |   .0502479   .0121998     4.12   0.000     .0263368     .074159
      lncprt |  -.3368709   .0613217    -5.49   0.000    -.4570592   -.2166825
        mjpw |   2.042298   .2876541     7.10   0.000     1.478506     2.60609
        cntg |   2.348584   .2576597     9.12   0.000      1.84358    2.853588
        dist |  -.5636394   .0829286    -6.80   0.000    -.7261764   -.4011024
    numstate |   .0076154   .0026654     2.86   0.004     .0023912    .0128395
  fatalyears |  -.7435186   .0425738   -17.46   0.000    -.8269618   -.6600755
 fatalyears2 |  -.0067765   .0005237   -12.94   0.000     -.007803     -.00575
 fatalyears3 |   .0046453   .0004138    11.23   0.000     .0038342    .0054563
 fatalyears4 |  -.0012658   .0001535    -8.24   0.000    -.0015668   -.0009649
       _cons |  -1.871716   .8349254    -2.24   0.025     -3.50814   -.2352922
------------------------------------------------------------------------------
(1 real change made)
specification was str3 now str4
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)

.  
. replace Dmlpvalues=2*normal(-abs(DmlCoefficient/DmlSE))
(8 real changes made)

. replace CIElpvalues=2*normal(-abs(CIElCoefficient/CIElSE))
(0 real changes made)

. keep specification-CIElpvalues

. drop if specnumber==.
(553779 observations deleted)

. saveold "robustness3.dta", replace
(note: file robustness3.dta not found)
file robustness3.dta saved

. 
. 
end of do-file

. *This produces robustness1.dta and robustness3.dta
. 
. do "12-09-24_bdmrobustness.do"

. *12-09-24_bdmrobustness.do
. 
. clear

. macro drop _all

. global filetree /Users/Allan/Dropbox/!!Papers/Liberal Peace/12-02-21_ISQ_commentary/DOR_ISQ_2013_Replication/M_R
> ep

. 
. cd "$filetree"
/Users/Allan/Dropbox/!!Papers/Liberal Peace/12-02-21_ISQ_commentary/DOR_ISQ_2013_Replication/M_Rep

. 
. set more off

. 
. use "MM_precise.dta", clear

. 
. sort ccode1 ccode2 year

. gen specification="NA"

. gen specnumber=.
(553787 missing values generated)

. gen n=.
(553787 missing values generated)

. gen bdmCoefficient=.
(553787 missing values generated)

. gen bdmSE=.
(553787 missing values generated)

. gen CIElCoefficient=.
(553787 missing values generated)

. gen CIElSE=.
(553787 missing values generated)

. gen bdmpvalues=.
(553787 missing values generated)

. gen CIElpvalues=.
(553787 missing values generated)

. 
. 
. gen bdmCIElc=bdm*CIElc
(231976 missing values generated)

. 
. sort ccode1 ccode2 year

. global controls lncprt mjpw cntg dist numstate 

. 
. global fmcontrols lncprt mjpw cntg dist numstate fpceyrs fspl1 fspl2 fspl3

. 
. global amcontrols lncprt mjpw cntg dist numstate apceyrs aspl1 aspl2 aspl3

. 
. local j=0

. 
. 
. **M2 with 4 dependent variables x interaction = 8 models
. local j=`j'+1

. local spec`j' "'"

. local covars`j' "mzmidonl CIEl  bdm                                        $controls midyears*"

. 
. 
. local j=`j'+1

. local spec`j' "O'"

. local covars`j' "mzmidol CIEl  bdm                                         $controls midyears*"

. 
. 
. local j=`j'+1

. local spec`j' "F'"

. local covars`j' "mzfmidonl CIEl  bdm                                       $controls fatalyears*"

. 
. 
. local j=`j'+1

. local spec`j' "FO'"

. local covars`j'  "mzfmidol CIEl  bdm                                       $controls fatalyears*"

. 
. 
. local j=`j'+1

. local spec`j'  "I'"

. local covars`j'  "mzmidonl CIElc  bdm bdmCIElc                                     $controls midyears*"

. 
. 
. local j=`j'+1

. local spec`j'  "OI'"

. local covars`j'  "mzmidol CIElc  bdm     bdmCIElc                                  $controls midyears*"

. 
. 
. local j=`j'+1

. local spec`j'  "FI'"

. local covars`j'  "mzfmidonl CIElc  bdm           bdmCIElc                          $controls fatalyears*"

. 
. 
. local j=`j'+1

. local spec`j'  "FOI'"

. local covars`j' "mzfmidol CIElc  bdm    bdmCIElc                                   $controls fatalyears*"

. 
. 
. 
. **M2 with DemocracyHigh, with 4 dependent variables x interaction = 8 models
. local j=`j'+1

. local spec`j' "D'"

. local covars`j' "mzmidonl CIEl  bdm  dmh                                           $controls midyears*"

. 
.  
. local j=`j'+1

. local spec`j'  "OD'"

. local covars`j'  "mzmidol CIEl  bdm  dmh                                           $controls midyears*"

. 
. 
. local j=`j'+1

. local spec`j'  "FD'"

. local covars`j'  "mzfmidonl CIEl  bdm  dmh                                         $controls fatalyears*"

. 
. 
. local j=`j'+1

. local spec`j'  "FOD'"

. local covars`j'  "mzfmidol CIEl  bdm  dmh                                          $controls fatalyears*"

. 
. 
. 
. local j=`j'+1

. local spec`j'  "ID'"

. local covars`j'  "mzmidonl CIElc  bdm bdmCIElc   dmh                               $controls midyears*"

. 
. 
. local j=`j'+1

. local spec`j'  "OID'"

. local covars`j'  "mzmidol CIElc  bdm     bdmCIElc        dmh                       $controls midyears*"

. 
. 
. local j=`j'+1

. local spec`j'  "FID'"

. local covars`j'  "mzfmidonl CIElc  bdm           bdmCIElc        dmh               $controls fatalyears*"

. 
. 
. local j=`j'+1

. local spec`j'  "FOID'"

. local covars`j'  "mzfmidol CIElc  bdm   bdmCIElc         dmh                       $controls fatalyears*"

. 
. 
. 
. 
. **Estimating Models, Saving Values
. forvalues k=1(1)`j' {
  2. logit `covars`k'', cl(ID) nolog
  3. replace n= e(N)   if _n==`k'
  4. replace specification="`spec`k''" if _n==`k'
  5. replace specnumber=`k' if _n==`k'
  6. replace bdmCoefficient=_b[bdm] if _n==`k'
  7. replace bdmSE=_se[bdm] if _n==`k'
  8. replace CIElCoefficient=_b[CIEl] if _n==`k'
  9. replace CIElSE=_se[CIEl] if _n==`k'
 10. }

Logistic regression                               Number of obs   =     321316
                                                  Wald chi2(11)   =    2548.92
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -3973.4998                 Pseudo R2       =     0.4096

                                 (Std. Err. adjusted for 12467 clusters in ID)
------------------------------------------------------------------------------
             |               Robust
    mzmidonl |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        CIEl |  -.2918777   .0615796    -4.74   0.000    -.4125715   -.1711839
         bdm |  -.4642076   .2688258    -1.73   0.084    -.9910964    .0626813
      lncprt |  -.2349774   .0476225    -4.93   0.000    -.3283158    -.141639
        mjpw |   1.944234   .2001775     9.71   0.000     1.551893    2.336575
        cntg |   2.785193    .219388    12.70   0.000       2.3552    3.215185
        dist |  -.4179955   .0843397    -4.96   0.000    -.5832982   -.2526928
    numstate |   .0035268   .0018402     1.92   0.055    -.0000799    .0071336
    midyears |  -.4745429   .0347688   -13.65   0.000    -.5426886   -.4063973
   midyears2 |   -.003428   .0004319    -7.94   0.000    -.0042746   -.0025814
   midyears3 |   .0018172   .0003083     5.89   0.000      .001213    .0024214
   midyears4 |  -.0004209   .0001198    -3.51   0.000    -.0006558   -.0001861
       _cons |   -1.36144   .7871112    -1.73   0.084     -2.90415    .1812698
------------------------------------------------------------------------------
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)

Logistic regression                               Number of obs   =     321811
                                                  Wald chi2(11)   =    2636.43
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood =  -5170.961                 Pseudo R2       =     0.4548

                                 (Std. Err. adjusted for 12469 clusters in ID)
------------------------------------------------------------------------------
             |               Robust
     mzmidol |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        CIEl |   -.412365   .0651869    -6.33   0.000    -.5401291    -.284601
         bdm |   -.485407   .2619524    -1.85   0.064    -.9988242    .0280102
      lncprt |  -.2584632   .0473521    -5.46   0.000    -.3512716   -.1656547
        mjpw |   2.190484   .1955559    11.20   0.000     1.807202    2.573767
        cntg |   2.157939   .1921891    11.23   0.000     1.781255    2.534622
        dist |  -.5182204    .089397    -5.80   0.000    -.6934354   -.3430055
    numstate |   .0048581   .0018201     2.67   0.008     .0012908    .0084254
    midyears |  -.6848128   .0355334   -19.27   0.000     -.754457   -.6151686
   midyears2 |  -.0056503   .0004254   -13.28   0.000     -.006484   -.0048166
   midyears3 |    .003144   .0002939    10.70   0.000      .002568      .00372
   midyears4 |   -.000757   .0001099    -6.89   0.000    -.0009724   -.0005417
       _cons |   .3869572   .7912347     0.49   0.625    -1.163834    1.937749
------------------------------------------------------------------------------
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)

Logistic regression                               Number of obs   =     321309
                                                  Wald chi2(11)   =    1127.57
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -1733.2747                 Pseudo R2       =     0.2921

                                 (Std. Err. adjusted for 12467 clusters in ID)
------------------------------------------------------------------------------
             |               Robust
   mzfmidonl |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        CIEl |  -.7352798   .1060876    -6.93   0.000    -.9432078   -.5273518
         bdm |  -1.058652   .5133781    -2.06   0.039    -2.064855   -.0524497
      lncprt |   -.346964   .0694911    -4.99   0.000     -.483164   -.2107641
        mjpw |    1.95402   .2869707     6.81   0.000     1.391568    2.516473
        cntg |   2.353387    .278083     8.46   0.000     1.808355     2.89842
        dist |  -.6237669   .1127296    -5.53   0.000    -.8447127    -.402821
    numstate |  -.0034358   .0025959    -1.32   0.186    -.0085236    .0016521
  fatalyears |  -.2844778   .0561676    -5.06   0.000    -.3945642   -.1743914
 fatalyears2 |   -.002053   .0006112    -3.36   0.001    -.0032509   -.0008551
 fatalyears3 |   .0015268   .0004625     3.30   0.001     .0006204    .0024332
 fatalyears4 |  -.0005729    .000161    -3.56   0.000    -.0008886   -.0002573
       _cons |   .3299171   1.091353     0.30   0.762    -1.809096     2.46893
------------------------------------------------------------------------------
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)

Logistic regression                               Number of obs   =     321568
                                                  Wald chi2(11)   =    1581.45
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -2428.1488                 Pseudo R2       =     0.4165

                                 (Std. Err. adjusted for 12469 clusters in ID)
------------------------------------------------------------------------------
             |               Robust
    mzfmidol |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        CIEl |  -.8773309   .1081225    -8.11   0.000    -1.089247   -.6654147
         bdm |  -.5634683   .5147856    -1.09   0.274    -1.572429    .4454929
      lncprt |  -.3386212   .0706245    -4.79   0.000    -.4770427   -.2001997
        mjpw |   2.224334   .3046126     7.30   0.000     1.627304    2.821364
        cntg |   1.907608   .2473315     7.71   0.000     1.422847    2.392368
        dist |  -.6387726    .097027    -6.58   0.000     -.828942   -.4486032
    numstate |   .0040179   .0025662     1.57   0.117    -.0010118    .0090476
  fatalyears |  -.7841515   .0476252   -16.47   0.000    -.8774952   -.6908079
 fatalyears2 |  -.0071361   .0005637   -12.66   0.000    -.0082409   -.0060314
 fatalyears3 |   .0048394   .0004361    11.10   0.000     .0039846    .0056942
 fatalyears4 |  -.0012846   .0001594    -8.06   0.000     -.001597   -.0009721
       _cons |   1.365081   .9924571     1.38   0.169    -.5800988    3.310262
------------------------------------------------------------------------------
(1 real change made)
specification was str2 now str3
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)

Logistic regression                               Number of obs   =     321316
                                                  Wald chi2(12)   =    2538.57
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -3968.2799                 Pseudo R2       =     0.4104

                                 (Std. Err. adjusted for 12467 clusters in ID)
------------------------------------------------------------------------------
             |               Robust
    mzmidonl |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       CIElc |  -.2013369   .0726355    -2.77   0.006    -.3436998    -.058974
         bdm |  -.3653123   .2311771    -1.58   0.114     -.818411    .0877865
    bdmCIElc |  -.2402983    .116099    -2.07   0.038    -.4678482   -.0127484
      lncprt |  -.2381936   .0465587    -5.12   0.000    -.3294469   -.1469402
        mjpw |   1.971333   .1937241    10.18   0.000     1.591641    2.351026
        cntg |   2.779004   .2143289    12.97   0.000     2.358927    3.199081
        dist |  -.4281576   .0815262    -5.25   0.000    -.5879461   -.2683691
    numstate |   .0033423   .0018536     1.80   0.071    -.0002907    .0069752
    midyears |  -.4734075   .0346741   -13.65   0.000    -.5413676   -.4054475
   midyears2 |  -.0034024   .0004322    -7.87   0.000    -.0042494   -.0025553
   midyears3 |   .0017975   .0003089     5.82   0.000     .0011921     .002403
   midyears4 |  -.0004145   .0001201    -3.45   0.001      -.00065   -.0001791
       _cons |  -1.638023   .7609231    -2.15   0.031    -3.129405   -.1466408
------------------------------------------------------------------------------
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)

Logistic regression                               Number of obs   =     321811
                                                  Wald chi2(12)   =    2663.62
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -5170.5057                 Pseudo R2       =     0.4549

                                 (Std. Err. adjusted for 12469 clusters in ID)
------------------------------------------------------------------------------
             |               Robust
     mzmidol |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       CIElc |  -.3884883   .0831019    -4.67   0.000    -.5513651   -.2256115
         bdm |  -.4708913   .2563392    -1.84   0.066    -.9733069    .0315243
    bdmCIElc |  -.0680347   .1193882    -0.57   0.569    -.3020313    .1659618
      lncprt |  -.2593409   .0468307    -5.54   0.000    -.3511274   -.1675543
        mjpw |   2.196026   .1918612    11.45   0.000     1.819985    2.572067
        cntg |   2.156534   .1906623    11.31   0.000     1.782843    2.530225
        dist |  -.5203279   .0885638    -5.88   0.000    -.6939097    -.346746
    numstate |   .0048097   .0018243     2.64   0.008      .001234    .0083853
    midyears |  -.6844967   .0355773   -19.24   0.000     -.754227   -.6147664
   midyears2 |  -.0056443   .0004265   -13.23   0.000    -.0064802   -.0048083
   midyears3 |   .0031397   .0002947    10.65   0.000     .0025621    .0037173
   midyears4 |  -.0007557   .0001101    -6.87   0.000    -.0009714     -.00054
       _cons |  -.2006132   .7651309    -0.26   0.793    -1.700242    1.299016
------------------------------------------------------------------------------
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)

Logistic regression                               Number of obs   =     321309
                                                  Wald chi2(12)   =    1265.11
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -1728.8631                 Pseudo R2       =     0.2939

                                 (Std. Err. adjusted for 12467 clusters in ID)
------------------------------------------------------------------------------
             |               Robust
   mzfmidonl |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       CIElc |  -.6503062   .1151534    -5.65   0.000    -.8760028   -.4246096
         bdm |  -1.608353   .4716369    -3.41   0.001    -2.532744   -.6839617
    bdmCIElc |  -1.202778   .2032851    -5.92   0.000     -1.60121   -.8043468
      lncprt |   -.348378   .0687653    -5.07   0.000    -.4831555   -.2136005
        mjpw |   1.967253   .2825289     6.96   0.000     1.413506    2.520999
        cntg |   2.355176   .2750527     8.56   0.000     1.816083    2.894269
        dist |  -.6278355   .1108456    -5.66   0.000    -.8450888   -.4105821
    numstate |  -.0031814    .002614    -1.22   0.224    -.0083046    .0019419
  fatalyears |  -.2853939   .0561092    -5.09   0.000    -.3953659   -.1754218
 fatalyears2 |  -.0020583   .0006118    -3.36   0.001    -.0032575   -.0008592
 fatalyears3 |   .0015336   .0004634     3.31   0.001     .0006254    .0024418
 fatalyears4 |  -.0005771   .0001618    -3.57   0.000    -.0008942   -.0002601
       _cons |  -.7161744    1.07575    -0.67   0.506    -2.824605    1.392256
------------------------------------------------------------------------------
Note: 1794 failures and 0 successes completely determined.
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)

Logistic regression                               Number of obs   =     321568
                                                  Wald chi2(12)   =    1583.51
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -2426.2832                 Pseudo R2       =     0.4170

                                 (Std. Err. adjusted for 12469 clusters in ID)
------------------------------------------------------------------------------
             |               Robust
    mzfmidol |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       CIElc |  -.8180657   .1272071    -6.43   0.000    -1.067387   -.5687443
         bdm |  -.7486539   .5375366    -1.39   0.164    -1.802206    .3048984
    bdmCIElc |  -.4542175    .225674    -2.01   0.044    -.8965305   -.0119045
      lncprt |  -.3398066   .0702025    -4.84   0.000     -.477401   -.2022122
        mjpw |    2.23433   .3005673     7.43   0.000     1.645229    2.823431
        cntg |   1.907674   .2457239     7.76   0.000     1.426064    2.389284
        dist |  -.6420984   .0960399    -6.69   0.000    -.8303332   -.4538637
    numstate |   .0041314   .0025683     1.61   0.108    -.0009024    .0091652
  fatalyears |  -.7847946   .0475757   -16.50   0.000    -.8780412    -.691548
 fatalyears2 |  -.0071414   .0005639   -12.66   0.000    -.0082467   -.0060362
 fatalyears3 |   .0048443   .0004363    11.10   0.000     .0039892    .0056993
 fatalyears4 |  -.0012867   .0001595    -8.07   0.000    -.0015992   -.0009741
       _cons |   .0941016   .9712701     0.10   0.923    -1.809553    1.997756
------------------------------------------------------------------------------
Note: 12 failures and 0 successes completely determined.
(1 real change made)
specification was str3 now str4
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)

Logistic regression                               Number of obs   =     321316
                                                  Wald chi2(12)   =    2533.51
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -3949.0385                 Pseudo R2       =     0.4133

                                 (Std. Err. adjusted for 12467 clusters in ID)
------------------------------------------------------------------------------
             |               Robust
    mzmidonl |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        CIEl |  -.3633541    .066589    -5.46   0.000     -.493866   -.2328421
         bdm |  -.5642086   .2398981    -2.35   0.019      -1.0344   -.0940169
         dmh |   .0398277   .0091887     4.33   0.000     .0218181    .0578372
      lncprt |  -.2415519   .0458107    -5.27   0.000    -.3313392   -.1517647
        mjpw |   1.892404   .1896614     9.98   0.000     1.520674    2.264133
        cntg |   2.890224   .2108903    13.70   0.000     2.476887    3.303562
        dist |  -.4269531   .0771373    -5.53   0.000    -.5781395   -.2757667
    numstate |   .0024733   .0018093     1.37   0.172    -.0010728    .0060194
    midyears |  -.4669023   .0343662   -13.59   0.000    -.5342587   -.3995459
   midyears2 |   -.003425   .0004324    -7.92   0.000    -.0042725   -.0025775
   midyears3 |   .0018372   .0003094     5.94   0.000     .0012308    .0024437
   midyears4 |  -.0004339   .0001204    -3.61   0.000    -.0006698    -.000198
       _cons |  -1.261862   .7314095    -1.73   0.084    -2.695398    .1716746
------------------------------------------------------------------------------
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)

Logistic regression                               Number of obs   =     321811
                                                  Wald chi2(12)   =    2695.95
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -5139.6541                 Pseudo R2       =     0.4581

                                 (Std. Err. adjusted for 12469 clusters in ID)
------------------------------------------------------------------------------
             |               Robust
     mzmidol |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        CIEl |  -.4836426   .0717015    -6.75   0.000    -.6241751   -.3431102
         bdm |  -.5998267   .2369958    -2.53   0.011     -1.06433   -.1353236
         dmh |   .0396054   .0092801     4.27   0.000     .0214167    .0577941
      lncprt |  -.2630127   .0456194    -5.77   0.000     -.352425   -.1736004
        mjpw |    2.11349    .181906    11.62   0.000     1.756961    2.470019
        cntg |   2.279292   .1884834    12.09   0.000     1.909872    2.648713
        dist |  -.5213638   .0832637    -6.26   0.000    -.6845577     -.35817
    numstate |   .0037433   .0017841     2.10   0.036     .0002465    .0072401
    midyears |  -.6764794   .0352343   -19.20   0.000    -.7455374   -.6074214
   midyears2 |  -.0056397   .0004243   -13.29   0.000    -.0064713   -.0048081
   midyears3 |   .0031587   .0002937    10.76   0.000     .0025831    .0037343
   midyears4 |  -.0007681     .00011    -6.98   0.000    -.0009837   -.0005525
       _cons |   .4501267   .7468411     0.60   0.547    -1.013655    1.913908
------------------------------------------------------------------------------
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)

Logistic regression                               Number of obs   =     321309
                                                  Wald chi2(12)   =    1074.42
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood =   -1721.27                 Pseudo R2       =     0.2970

                                 (Std. Err. adjusted for 12467 clusters in ID)
------------------------------------------------------------------------------
             |               Robust
   mzfmidonl |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        CIEl |  -.8423846   .1052967    -8.00   0.000    -1.048762   -.6360068
         bdm |  -1.176919   .4798309    -2.45   0.014     -2.11737   -.2364677
         dmh |   .0456367    .014196     3.21   0.001      .017813    .0734603
      lncprt |  -.3508005   .0670402    -5.23   0.000    -.4821968   -.2194041
        mjpw |   1.873986    .279993     6.69   0.000      1.32521    2.422762
        cntg |    2.47167   .2710952     9.12   0.000     1.940333    3.003007
        dist |  -.6285203   .1029053    -6.11   0.000     -.830211   -.4268297
    numstate |  -.0044064   .0026048    -1.69   0.091    -.0095117    .0006988
  fatalyears |  -.2696123   .0564372    -4.78   0.000    -.3802272   -.1589973
 fatalyears2 |  -.0019873   .0006174    -3.22   0.001    -.0031973   -.0007772
 fatalyears3 |   .0015079   .0004668     3.23   0.001      .000593    .0024228
 fatalyears4 |  -.0005768   .0001624    -3.55   0.000    -.0008951   -.0002584
       _cons |   .3677503     1.0057     0.37   0.715    -1.603385    2.338885
------------------------------------------------------------------------------
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)

Logistic regression                               Number of obs   =     321568
                                                  Wald chi2(12)   =    1645.99
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -2409.8846                 Pseudo R2       =     0.4209

                                 (Std. Err. adjusted for 12469 clusters in ID)
------------------------------------------------------------------------------
             |               Robust
    mzfmidol |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        CIEl |   -.983409   .1119683    -8.78   0.000    -1.202863   -.7639553
         bdm |  -.7177546   .4843225    -1.48   0.138    -1.667009    .2315002
         dmh |   .0461197   .0138913     3.32   0.001     .0188932    .0733462
      lncprt |  -.3476338   .0687384    -5.06   0.000    -.4823586    -.212909
        mjpw |    2.13092   .2816476     7.57   0.000     1.578901    2.682939
        cntg |     2.0649   .2507887     8.23   0.000     1.573363    2.556436
        dist |   -.625641     .09036    -6.92   0.000    -.8027433   -.4485388
    numstate |   .0029715   .0026217     1.13   0.257    -.0021669      .00811
  fatalyears |  -.7726975   .0478015   -16.16   0.000    -.8663867   -.6790083
 fatalyears2 |  -.0071118   .0005635   -12.62   0.000    -.0082162   -.0060074
 fatalyears3 |   .0048511   .0004356    11.14   0.000     .0039973    .0057049
 fatalyears4 |  -.0012976   .0001598    -8.12   0.000    -.0016108   -.0009844
       _cons |   1.286984   .9570828     1.34   0.179    -.5888643    3.162831
------------------------------------------------------------------------------
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)

Logistic regression                               Number of obs   =     321316
                                                  Wald chi2(13)   =    2532.69
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -3947.3395                 Pseudo R2       =     0.4135

                                 (Std. Err. adjusted for 12467 clusters in ID)
------------------------------------------------------------------------------
             |               Robust
    mzmidonl |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       CIElc |  -.3075225   .0817345    -3.76   0.000    -.4677192   -.1473259
         bdm |  -.5065713    .223839    -2.26   0.024    -.9452876    -.067855
    bdmCIElc |  -.1417359   .1190507    -1.19   0.234    -.3750709    .0915991
         dmh |   .0376961   .0093389     4.04   0.000     .0193922        .056
      lncprt |  -.2430861   .0452905    -5.37   0.000    -.3318539   -.1543183
        mjpw |   1.910707   .1852159    10.32   0.000     1.547691    2.273724
        cntg |   2.879749   .2081787    13.83   0.000     2.471727    3.287772
        dist |  -.4322716   .0761021    -5.68   0.000     -.581429   -.2831141
    numstate |   .0024182   .0018178     1.33   0.183    -.0011445     .005981
    midyears |  -.4665992   .0343246   -13.59   0.000    -.5338742   -.3993241
   midyears2 |  -.0034104   .0004332    -7.87   0.000    -.0042595   -.0025613
   midyears3 |   .0018244   .0003103     5.88   0.000     .0012163    .0024326
   midyears4 |  -.0004291   .0001207    -3.56   0.000    -.0006656   -.0001926
       _cons |  -1.717726   .7211478    -2.38   0.017    -3.131149   -.3043019
------------------------------------------------------------------------------
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)

Logistic regression                               Number of obs   =     321811
                                                  Wald chi2(13)   =    2710.67
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -5139.5549                 Pseudo R2       =     0.4581

                                 (Std. Err. adjusted for 12469 clusters in ID)
------------------------------------------------------------------------------
             |               Robust
     mzmidol |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       CIElc |  -.4954352   .0923725    -5.36   0.000     -.676482   -.3143884
         bdm |  -.6066716   .2467119    -2.46   0.014    -1.090218   -.1231251
    bdmCIElc |   .0327487   .1226678     0.27   0.789    -.2076758    .2731733
         dmh |   .0399953   .0094938     4.21   0.000     .0213879    .0586027
      lncprt |  -.2626396   .0453931    -5.79   0.000    -.3516084   -.1736707
        mjpw |   2.110108   .1786303    11.81   0.000     1.759999    2.460217
        cntg |   2.281366   .1875509    12.16   0.000     1.913773    2.648959
        dist |  -.5204342   .0832052    -6.25   0.000    -.6835134   -.3573549
    numstate |   .0037546   .0017836     2.11   0.035     .0002587    .0072504
    midyears |   -.676547   .0352736   -19.18   0.000    -.7456819    -.607412
   midyears2 |  -.0056422   .0004255   -13.26   0.000    -.0064763   -.0048082
   midyears3 |   .0031609   .0002945    10.73   0.000     .0025836    .0037381
   midyears4 |  -.0007688   .0001102    -6.98   0.000    -.0009848   -.0005528
       _cons |  -.3109776   .7332593    -0.42   0.671    -1.748139    1.126184
------------------------------------------------------------------------------
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)

Logistic regression                               Number of obs   =     321309
                                                  Wald chi2(13)   =    1278.62
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -1717.9588                 Pseudo R2       =     0.2984

                                 (Std. Err. adjusted for 12467 clusters in ID)
------------------------------------------------------------------------------
             |               Robust
   mzfmidonl |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       CIElc |  -.7691112   .1118971    -6.87   0.000    -.9884255   -.5497969
         bdm |  -1.692726   .4522769    -3.74   0.000    -2.579173   -.8062797
    bdmCIElc |  -1.047101   .2111018    -4.96   0.000    -1.460853   -.6333489
         dmh |   .0437915   .0141284     3.10   0.002     .0161004    .0714827
      lncprt |  -.3519364   .0665188    -5.29   0.000    -.4823109   -.2215619
        mjpw |   1.887281   .2764967     6.83   0.000     1.345357    2.429204
        cntg |   2.468352   .2682261     9.20   0.000     1.942638    2.994065
        dist |  -.6314131   .1018136    -6.20   0.000    -.8309642   -.4318621
    numstate |  -.0041502   .0026265    -1.58   0.114    -.0092982    .0009977
  fatalyears |  -.2709788   .0562609    -4.82   0.000    -.3812482   -.1607094
 fatalyears2 |  -.0019964    .000617    -3.24   0.001    -.0032057   -.0007871
 fatalyears3 |   .0015164    .000467     3.25   0.001      .000601    .0024317
 fatalyears4 |   -.000581   .0001629    -3.57   0.000    -.0009003   -.0002617
       _cons |  -.8543988   .9964127    -0.86   0.391    -2.807332    1.098534
------------------------------------------------------------------------------
Note: 1553 failures and 0 successes completely determined.
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)

Logistic regression                               Number of obs   =     321568
                                                  Wald chi2(13)   =    1649.61
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood =  -2408.961                 Pseudo R2       =     0.4211

                                 (Std. Err. adjusted for 12469 clusters in ID)
------------------------------------------------------------------------------
             |               Robust
    mzfmidol |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       CIElc |  -.9413904   .1298042    -7.25   0.000    -1.195802   -.6869788
         bdm |  -.8602823   .5216554    -1.65   0.099    -1.882708    .1621435
    bdmCIElc |  -.3257102   .2266413    -1.44   0.151     -.769919    .1184987
         dmh |   .0451808   .0139699     3.23   0.001     .0178003    .0725613
      lncprt |  -.3481788   .0685601    -5.08   0.000    -.4825542   -.2138033
        mjpw |   2.139288    .278861     7.67   0.000      1.59273    2.685845
        cntg |   2.061457   .2490563     8.28   0.000     1.573316    2.549599
        dist |  -.6281272   .0900829    -6.97   0.000    -.8046865    -.451568
    numstate |   .0030792   .0026316     1.17   0.242    -.0020786    .0082371
  fatalyears |  -.7733754   .0476914   -16.22   0.000    -.8668489   -.6799019
 fatalyears2 |  -.0071171    .000563   -12.64   0.000    -.0082206   -.0060136
 fatalyears3 |   .0048554   .0004353    11.15   0.000     .0040022    .0057085
 fatalyears4 |  -.0012992   .0001597    -8.14   0.000    -.0016123   -.0009862
       _cons |  -.1662195   .9477801    -0.18   0.861    -2.023834    1.691395
------------------------------------------------------------------------------
Note: 10 failures and 0 successes completely determined.
(1 real change made)
specification was str4 now str5
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)

.  
. 
. replace bdmpvalues=2*normal(-abs(bdmCoefficient/bdmSE))
(16 real changes made)

. replace CIElpvalues=2*normal(-abs(CIElCoefficient/CIElSE))
(16 real changes made)

. keep specification-CIElpvalues

. drop if specnumber==.
(553771 observations deleted)

. saveold "robustnessbdm.dta", replace
(note: file robustnessbdm.dta not found)
file robustnessbdm.dta saved

. 
. 
. 
. 
. 
. **Analyses with no ln(life insurance) or GDP
. 
. clear

. macro drop _all

. global filetree /Users/Allan/Dropbox/!!Papers/Liberal Peace/12-02-21_ISQ_commentary/DOR_ISQ_2013_Replication/M_R
> ep

. 
. cd "$filetree"
/Users/Allan/Dropbox/!!Papers/Liberal Peace/12-02-21_ISQ_commentary/DOR_ISQ_2013_Replication/M_Rep

. 
. set more off

. 
. use "MM_precise.dta", clear

. 
. sort ccode1 ccode2 year

. gen specification="NA"

. 
. gen specnumber=.
(553787 missing values generated)

. gen n=.
(553787 missing values generated)

. gen bdmCoefficient=.
(553787 missing values generated)

. gen bdmSE=.
(553787 missing values generated)

. gen CIElCoefficient=.
(553787 missing values generated)

. gen CIElSE=.
(553787 missing values generated)

. gen bdmpvalues=.
(553787 missing values generated)

. gen CIElpvalues=.
(553787 missing values generated)

. 
. sort ccode1 ccode2 year

. global controls lncprt mjpw cntg dist numstate 

. 
. global fmcontrols lncprt mjpw cntg dist numstate fpceyrs fspl1 fspl2 fspl3

. 
. global amcontrols lncprt mjpw cntg dist numstate apceyrs aspl1 aspl2 aspl3

. 
. local j=0

. 
. **M1 with 4 dependent variables x DemocracyHigh = 8 models
. local j=`j'+1

. local spec`j' "n'"

. local covars`j' "mzmidonl   bdm                                            $controls midyears*"

. 
. local j=`j'+1

. local spec`j' "On'"

. local covars`j' "mzmidol   bdm                                     $controls midyears*"

. 
. local j=`j'+1

. local spec`j' "Fn'"

. local covars`j' "mzfmidonl   bdm                                           $controls fatalyears*"

. 
. local j=`j'+1

. local spec`j' "FOn'"

. local covars`j' "mzfmidol   bdm                                            $controls fatalyears*"

. 
. 
. local j=`j'+1

. local spec`j' "Dn'"

. local covars`j' "mzmidonl   bdm  dmh                                       $controls midyears*"

. 
. local j=`j'+1

. local spec`j' "ODn'"

. local covars`j' "mzmidol   bdm  dmh                                        $controls midyears*"

. 
. local j=`j'+1

. local spec`j' "FDn'"

. local covars`j' "mzfmidonl   bdm  dmh                                      $controls fatalyears*"

. 
. local j=`j'+1

. local spec`j' "FODn'"

. local covars`j' "mzfmidol   bdm  dmh                                       $controls fatalyears*"

. 
. 
. 
. **Estimating Models, Saving Values
. forvalues k=1(1)`j' {
  2. logit `covars`k'', cl(ID) nolog
  3. replace n= e(N)   if _n==`k'
  4. replace specification="`spec`k''" if _n==`k'
  5. replace specnumber=`k' if _n==`k'
  6. replace bdmCoefficient=_b[bdm] if _n==`k'
  7. replace bdmSE=_se[bdm] if _n==`k'
  8. *replace CIElCoefficient=_b[CIEl] if _n==`k'
. *replace CIElSE=_se[CIEl] if _n==`k'
. }

Logistic regression                               Number of obs   =     391599
                                                  Wald chi2(10)   =    3062.65
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -4851.7864                 Pseudo R2       =     0.3956

                                 (Std. Err. adjusted for 13538 clusters in ID)
------------------------------------------------------------------------------
             |               Robust
    mzmidonl |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         bdm |  -1.214943   .2586776    -4.70   0.000    -1.721942   -.7079442
      lncprt |  -.2112929   .0419193    -5.04   0.000    -.2934533   -.1291324
        mjpw |   1.845645   .1859718     9.92   0.000     1.481147    2.210143
        cntg |   2.968422   .1973229    15.04   0.000     2.581676    3.355167
        dist |  -.3819484   .0665901    -5.74   0.000    -.5124626   -.2514341
    numstate |   .0040599   .0017113     2.37   0.018     .0007059    .0074139
    midyears |  -.4467346   .0307907   -14.51   0.000    -.5070832    -.386386
   midyears2 |  -.0032834    .000389    -8.44   0.000    -.0040458    -.002521
   midyears3 |   .0018027   .0002834     6.36   0.000     .0012471    .0023582
   midyears4 |  -.0004544   .0001139    -3.99   0.000    -.0006777   -.0002312
       _cons |  -2.203736   .6487931    -3.40   0.001    -3.475347   -.9321246
------------------------------------------------------------------------------
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)

Logistic regression                               Number of obs   =     392172
                                                  Wald chi2(10)   =    2882.69
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -6399.5865                 Pseudo R2       =     0.4303

                                 (Std. Err. adjusted for 13538 clusters in ID)
------------------------------------------------------------------------------
             |               Robust
     mzmidol |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         bdm |  -1.416663    .288902    -4.90   0.000      -1.9829   -.8504252
      lncprt |  -.2386471   .0419672    -5.69   0.000    -.3209013    -.156393
        mjpw |   2.091458   .1883084    11.11   0.000      1.72238    2.460535
        cntg |   2.391247   .1813577    13.19   0.000     2.035793    2.746702
        dist |  -.4701201    .072735    -6.46   0.000    -.6126781   -.3275621
    numstate |   .0047869   .0017017     2.81   0.005     .0014516    .0081222
    midyears |  -.6426755   .0318204   -20.20   0.000    -.7050423   -.5803087
   midyears2 |  -.0053622   .0003837   -13.97   0.000    -.0061143   -.0046101
   midyears3 |   .0030473   .0002698    11.30   0.000     .0025185    .0035761
   midyears4 |  -.0007714   .0001042    -7.40   0.000    -.0009757   -.0005672
       _cons |  -.5850374   .6694151    -0.87   0.382    -1.897067    .7269922
------------------------------------------------------------------------------
(1 real change made)
specification was str2 now str3
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)

Logistic regression                               Number of obs   =     391550
                                                  Wald chi2(10)   =    1171.50
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -2116.8462                 Pseudo R2       =     0.2677

                                 (Std. Err. adjusted for 13538 clusters in ID)
------------------------------------------------------------------------------
             |               Robust
   mzfmidonl |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         bdm |  -2.508972   .5221565    -4.81   0.000     -3.53238   -1.485564
      lncprt |  -.3143077   .0598666    -5.25   0.000    -.4316441   -.1969714
        mjpw |   1.740745   .2700864     6.45   0.000     1.211385    2.270105
        cntg |   2.529136   .2636223     9.59   0.000     2.012446    3.045826
        dist |  -.5544895   .0966063    -5.74   0.000    -.7438344   -.3651446
    numstate |  -.0004905   .0024924    -0.20   0.844    -.0053754    .0043945
  fatalyears |  -.3049741   .0478034    -6.38   0.000     -.398667   -.2112811
 fatalyears2 |  -.0023894    .000545    -4.38   0.000    -.0034577   -.0013211
 fatalyears3 |   .0017812   .0004223     4.22   0.000     .0009535     .002609
 fatalyears4 |  -.0006395   .0001515    -4.22   0.000    -.0009365   -.0003426
       _cons |  -1.244601   .9796211    -1.27   0.204    -3.164623    .6754212
------------------------------------------------------------------------------
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)

Logistic regression                               Number of obs   =     391837
                                                  Wald chi2(10)   =    1452.19
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -2991.5247                 Pseudo R2       =     0.3773

                                 (Std. Err. adjusted for 13538 clusters in ID)
------------------------------------------------------------------------------
             |               Robust
    mzfmidol |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         bdm |  -2.087193   .6018351    -3.47   0.001    -3.266768   -.9076181
      lncprt |  -.3575949    .064311    -5.56   0.000    -.4836421   -.2315477
        mjpw |   2.060711    .306166     6.73   0.000     1.460637    2.660786
        cntg |   2.251898   .2548905     8.83   0.000     1.752322    2.751474
        dist |  -.5485251   .0904814    -6.06   0.000    -.7258653   -.3711848
    numstate |   .0038942   .0025649     1.52   0.129    -.0011328    .0089213
  fatalyears |  -.7447566   .0425034   -17.52   0.000    -.8280616   -.6614515
 fatalyears2 |  -.0068348    .000524   -13.04   0.000    -.0078618   -.0058077
 fatalyears3 |   .0046698   .0004123    11.33   0.000     .0038617    .0054778
 fatalyears4 |  -.0012566   .0001524    -8.25   0.000    -.0015552    -.000958
       _cons |  -.1825414   .9338119    -0.20   0.845    -2.012779    1.647696
------------------------------------------------------------------------------
(1 real change made)
specification was str3 now str4
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)

Logistic regression                               Number of obs   =     391599
                                                  Wald chi2(11)   =    3070.15
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -4838.5488                 Pseudo R2       =     0.3973

                                 (Std. Err. adjusted for 13538 clusters in ID)
------------------------------------------------------------------------------
             |               Robust
    mzmidonl |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         bdm |  -1.368181   .2491426    -5.49   0.000    -1.856491   -.8798704
         dmh |   .0252774   .0076021     3.33   0.001     .0103774    .0401773
      lncprt |  -.2119155   .0404581    -5.24   0.000     -.291212    -.132619
        mjpw |    1.77154   .1804859     9.82   0.000     1.417794    2.125286
        cntg |   3.040628   .1927458    15.78   0.000     2.662854    3.418403
        dist |  -.3791122   .0625665    -6.06   0.000    -.5017403   -.2564841
    numstate |   .0032809     .00172     1.91   0.056    -.0000902     .006652
    midyears |   -.442213   .0305033   -14.50   0.000    -.5019984   -.3824276
   midyears2 |  -.0032667   .0003889    -8.40   0.000    -.0040289   -.0025045
   midyears3 |   .0018045   .0002843     6.35   0.000     .0012472    .0023618
   midyears4 |  -.0004597   .0001144    -4.02   0.000    -.0006839   -.0002354
       _cons |  -2.200923   .6209509    -3.54   0.000    -3.417964   -.9838812
------------------------------------------------------------------------------
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)

Logistic regression                               Number of obs   =     392172
                                                  Wald chi2(11)   =    2897.31
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -6386.9354                 Pseudo R2       =     0.4314

                                 (Std. Err. adjusted for 13538 clusters in ID)
------------------------------------------------------------------------------
             |               Robust
     mzmidol |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         bdm |  -1.541592   .2781474    -5.54   0.000    -2.086751   -.9964333
         dmh |   .0216357   .0074606     2.90   0.004     .0070132    .0362583
      lncprt |  -.2392944   .0410266    -5.83   0.000    -.3197051   -.1588837
        mjpw |   2.022204   .1823536    11.09   0.000     1.664797     2.37961
        cntg |    2.45896   .1790976    13.73   0.000     2.107935    2.809984
        dist |  -.4658262   .0693107    -6.72   0.000    -.6016725   -.3299798
    numstate |     .00406   .0017066     2.38   0.017     .0007151    .0074049
    midyears |  -.6387973   .0316572   -20.18   0.000    -.7008443   -.5767504
   midyears2 |  -.0053474   .0003835   -13.94   0.000    -.0060991   -.0045957
   midyears3 |   .0030478   .0002703    11.28   0.000     .0025181    .0035775
   midyears4 |  -.0007752   .0001046    -7.41   0.000    -.0009801   -.0005703
       _cons |  -.5868789   .6445709    -0.91   0.363    -1.850215    .6764569
------------------------------------------------------------------------------
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)

Logistic regression                               Number of obs   =     391550
                                                  Wald chi2(11)   =    1168.21
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -2113.8474                 Pseudo R2       =     0.2688

                                 (Std. Err. adjusted for 13538 clusters in ID)
------------------------------------------------------------------------------
             |               Robust
   mzfmidonl |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         bdm |  -2.601714   .5008946    -5.19   0.000     -3.58345   -1.619979
         dmh |   .0201329   .0127322     1.58   0.114    -.0048217    .0450875
      lncprt |  -.3131886   .0591243    -5.30   0.000      -.42907   -.1973071
        mjpw |   1.670291   .2731417     6.12   0.000     1.134943    2.205639
        cntg |   2.585006   .2641384     9.79   0.000     2.067305    3.102708
        dist |  -.5491332   .0928583    -5.91   0.000    -.7311321   -.3671342
    numstate |  -.0010684   .0025485    -0.42   0.675    -.0060633    .0039265
  fatalyears |  -.2990611   .0478095    -6.26   0.000     -.392766   -.2053561
 fatalyears2 |  -.0023492   .0005472    -4.29   0.000    -.0034217   -.0012767
 fatalyears3 |   .0017615   .0004241     4.15   0.000     .0009303    .0025927
 fatalyears4 |  -.0006371   .0001522    -4.18   0.000    -.0009355   -.0003387
       _cons |  -1.273424   .9467311    -1.35   0.179    -3.128982    .5821352
------------------------------------------------------------------------------
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)

Logistic regression                               Number of obs   =     391837
                                                  Wald chi2(11)   =    1477.80
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood =  -2988.141                 Pseudo R2       =     0.3780

                                 (Std. Err. adjusted for 13538 clusters in ID)
------------------------------------------------------------------------------
             |               Robust
    mzfmidol |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         bdm |  -2.171408   .5817608    -3.73   0.000    -3.311638   -1.031177
         dmh |   .0174145   .0120921     1.44   0.150    -.0062856    .0411145
      lncprt |  -.3585985   .0635695    -5.64   0.000    -.4831925   -.2340046
        mjpw |    2.00324      .2987     6.71   0.000     1.417799    2.588681
        cntg |   2.312848   .2586758     8.94   0.000     1.805853    2.819843
        dist |  -.5380633   .0896464    -6.00   0.000    -.7137671   -.3623595
    numstate |   .0032876   .0026624     1.23   0.217    -.0019306    .0085058
  fatalyears |  -.7408117   .0427634   -17.32   0.000    -.8246264   -.6569969
 fatalyears2 |  -.0068118   .0005257   -12.96   0.000    -.0078421   -.0057815
 fatalyears3 |   .0046604    .000413    11.29   0.000      .003851    .0054698
 fatalyears4 |  -.0012564   .0001527    -8.23   0.000    -.0015556   -.0009572
       _cons |  -.2327782   .9197274    -0.25   0.800    -2.035411    1.569854
------------------------------------------------------------------------------
(1 real change made)
specification was str4 now str5
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)

.  
. replace bdmpvalues=2*normal(-abs(bdmCoefficient/bdmSE))
(8 real changes made)

. replace CIElpvalues=2*normal(-abs(CIElCoefficient/CIElSE))
(0 real changes made)

. keep specification-CIElpvalues

. drop if specnumber==.
(553779 observations deleted)

. saveold "robustnessbdm2.dta", replace
(note: file robustnessbdm2.dta not found)
file robustnessbdm2.dta saved

. 
. 
end of do-file

. *This produces robustnessbdm.dta and robustnessbdm2.dta
. 
. do "12-09-24_h10dmrobustness.do"

. *12-09-24_h10dmrobustness.do
. 
. clear

. macro drop _all

. global filetree /Users/Allan/Dropbox/!!Papers/Liberal Peace/12-02-21_ISQ_commentary/DOR_ISQ_2013_Replication/M_R
> ep

. 
. cd "$filetree"
/Users/Allan/Dropbox/!!Papers/Liberal Peace/12-02-21_ISQ_commentary/DOR_ISQ_2013_Replication/M_Rep

. 
. set more off

. 
. use "MM_precise.dta", clear

. 
. sort ccode1 ccode2 year

. gen specification="NA"

. gen specnumber=.
(553787 missing values generated)

. gen n=.
(553787 missing values generated)

. gen h10dmCoefficient=.
(553787 missing values generated)

. gen h10dmSE=.
(553787 missing values generated)

. 
. gen CIElCoefficient=.
(553787 missing values generated)

. gen CIElSE=.
(553787 missing values generated)

. gen h10dmpvalues=.
(553787 missing values generated)

. gen CIElpvalues=.
(553787 missing values generated)

. 
. 
. gen h10dmCIElc=h10dm*CIElc
(231976 missing values generated)

. 
. sort ccode1 ccode2 year

. global controls lncprt mjpw cntg dist numstate 

. 
. global fmcontrols lncprt mjpw cntg dist numstate fpceyrs fspl1 fspl2 fspl3

. 
. global amcontrols lncprt mjpw cntg dist numstate apceyrs aspl1 aspl2 aspl3

. 
. local j=0

. 
. 
. **M2 with 4 dependent variables x interaction = 8 models
. local j=`j'+1

. local spec`j' "''"

. local covars`j' "mzmidonl CIEl  h10dm                                      $controls midyears*"

. 
. 
. local j=`j'+1

. local spec`j' "O''"

. local covars`j' "mzmidol CIEl  h10dm                                       $controls midyears*"

. 
. 
. local j=`j'+1

. local spec`j' "F''"

. local covars`j' "mzfmidonl CIEl  h10dm                                     $controls fatalyears*"

. 
. 
. local j=`j'+1

. local spec`j' "FO''"

. local covars`j'  "mzfmidol CIEl  h10dm                                     $controls fatalyears*"

. 
. 
. local j=`j'+1

. local spec`j'  "I''"

. local covars`j'  "mzmidonl CIElc  h10dm h10dmCIElc                                         $controls midyears*"

. 
. 
. local j=`j'+1

. local spec`j'  "OI''"

. local covars`j'  "mzmidol CIElc  h10dm   h10dmCIElc                                $controls midyears*"

. 
. 
. local j=`j'+1

. local spec`j'  "FI''"

. local covars`j'  "mzfmidonl CIElc  h10dm                 h10dmCIElc                        $controls fatalyears*
> "

. 
. 
. local j=`j'+1

. local spec`j'  "FOI''"

. local covars`j' "mzfmidol CIElc  h10dm          h10dmCIElc                                 $controls fatalyears*
> "

. 
. 
. 
. **M2 with DemocracyHigh, with 4 dependent variables x interaction = 8 models
. local j=`j'+1

. local spec`j' "D''"

. local covars`j' "mzmidonl CIEl  h10dm  dmh                                         $controls midyears*"

. 
.  
. local j=`j'+1

. local spec`j'  "OD''"

. local covars`j'  "mzmidol CIEl  h10dm  dmh                                         $controls midyears*"

. 
. 
. local j=`j'+1

. local spec`j'  "FD''"

. local covars`j'  "mzfmidonl CIEl  h10dm  dmh                                       $controls fatalyears*"

. 
. local j=`j'+1

. local spec`j'  "FOD''"

. local covars`j'  "mzfmidol CIEl  h10dm  dmh                                        $controls fatalyears*"

. 
. 
. 
. local j=`j'+1

. local spec`j'  "ID''"

. local covars`j'  "mzmidonl CIElc  h10dm h10dmCIElc       dmh                               $controls midyears*"

. 
. 
. local j=`j'+1

. local spec`j'  "OID''"

. local covars`j'  "mzmidol CIElc  h10dm   h10dmCIElc      dmh                       $controls midyears*"

. 
. 
. local j=`j'+1

. local spec`j'  "FID''"

. local covars`j'  "mzfmidonl CIElc  h10dm                 h10dmCIElc      dmh               $controls fatalyears*
> "

. 
. 
. local j=`j'+1

. local spec`j'  "FOID''"

. local covars`j'  "mzfmidol CIElc  h10dm         h10dmCIElc       dmh                       $controls fatalyears*
> "

. 
. 
. 
. 
. **Estimating Models, Saving Values
. forvalues k=1(1)`j' {
  2. logit `covars`k'', cl(ID) nolog
  3. replace n= e(N)   if _n==`k'
  4. replace specification="`spec`k'''" if _n==`k'
  5. replace specnumber=`k' if _n==`k'
  6. replace h10dmCoefficient=_b[h10dm] if _n==`k'
  7. replace h10dmSE=_se[h10dm] if _n==`k'
  8. replace CIElCoefficient=_b[CIEl] if _n==`k'
  9. replace CIElSE=_se[CIEl] if _n==`k'
 10. }

Logistic regression                               Number of obs   =     321316
                                                  Wald chi2(11)   =    2452.07
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -3974.3386                 Pseudo R2       =     0.4095

                                 (Std. Err. adjusted for 12467 clusters in ID)
------------------------------------------------------------------------------
             |               Robust
    mzmidonl |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        CIEl |  -.2984368   .0610302    -4.89   0.000    -.4180538   -.1788197
       h10dm |  -.8114468   .4362422    -1.86   0.063    -1.666466    .0435721
      lncprt |  -.2320764   .0471426    -4.92   0.000    -.3244742   -.1396785
        mjpw |   1.946714   .1980574     9.83   0.000     1.558529      2.3349
        cntg |   2.787875   .2159507    12.91   0.000     2.364619    3.211131
        dist |   -.417754   .0833316    -5.01   0.000    -.5810809   -.2544272
    numstate |   .0030075    .001875     1.60   0.109    -.0006675    .0066826
    midyears |  -.4723815   .0346145   -13.65   0.000    -.5402246   -.4045383
   midyears2 |  -.0034046   .0004312    -7.90   0.000    -.0042497   -.0025595
   midyears3 |   .0017925   .0003074     5.83   0.000     .0011899     .002395
   midyears4 |   -.000405    .000119    -3.40   0.001    -.0006382   -.0001719
       _cons |   -1.31063   .7842503    -1.67   0.095    -2.847733    .2264719
------------------------------------------------------------------------------
(1 real change made)
specification was str2 now str3
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)

Logistic regression                               Number of obs   =     321811
                                                  Wald chi2(11)   =    2534.41
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -5175.3035                 Pseudo R2       =     0.4544

                                 (Std. Err. adjusted for 12469 clusters in ID)
------------------------------------------------------------------------------
             |               Robust
     mzmidol |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        CIEl |  -.4321504   .0674596    -6.41   0.000    -.5643688    -.299932
       h10dm |  -.6852514   .4430661    -1.55   0.122    -1.553645    .1831422
      lncprt |  -.2582579   .0466431    -5.54   0.000    -.3496767   -.1668391
        mjpw |   2.195596   .1930875    11.37   0.000     1.817152    2.574041
        cntg |   2.161949   .1887955    11.45   0.000     1.791916    2.531981
        dist |  -.5169317   .0886546    -5.83   0.000    -.6906915   -.3431719
    numstate |   .0043861   .0018386     2.39   0.017     .0007825    .0079898
    midyears |  -.6823121   .0354101   -19.27   0.000    -.7517146   -.6129097
   midyears2 |  -.0056237   .0004247   -13.24   0.000    -.0064561   -.0047914
   midyears3 |   .0031166   .0002933    10.63   0.000     .0025417    .0036914
   midyears4 |    -.00074   .0001094    -6.77   0.000    -.0009544   -.0005256
       _cons |   .4312314   .7895213     0.55   0.585    -1.116202    1.978665
------------------------------------------------------------------------------
(1 real change made)
specification was str3 now str4
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
note: h10dm != 0 predicts failure perfectly
      h10dm dropped and 11309 obs not used


Logistic regression                               Number of obs   =     310000
                                                  Wald chi2(10)   =    1112.57
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -1736.8306                 Pseudo R2       =     0.2874

                                 (Std. Err. adjusted for 12188 clusters in ID)
------------------------------------------------------------------------------
             |               Robust
   mzfmidonl |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        CIEl |  -.7904655   .1068899    -7.40   0.000     -.999966   -.5809651
       h10dm |          0  (omitted)
      lncprt |  -.3475746   .0685308    -5.07   0.000    -.4818925   -.2132567
        mjpw |   1.972111   .2804937     7.03   0.000     1.422353    2.521869
        cntg |   2.355697   .2730338     8.63   0.000      1.82056    2.890833
        dist |  -.6223836   .1125598    -5.53   0.000    -.8429967   -.4017706
    numstate |  -.0038161   .0026283    -1.45   0.147    -.0089675    .0013353
  fatalyears |  -.2816843    .056324    -5.00   0.000    -.3920773   -.1712914
 fatalyears2 |  -.0020118   .0006133    -3.28   0.001    -.0032137   -.0008098
 fatalyears3 |   .0014695   .0004643     3.17   0.002     .0005595    .0023795
 fatalyears4 |  -.0005347   .0001616    -3.31   0.001    -.0008514    -.000218
       _cons |   .3640443   1.098314     0.33   0.740    -1.788612      2.5167
------------------------------------------------------------------------------
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
note: h10dm != 0 predicts failure perfectly
      h10dm dropped and 11309 obs not used


Logistic regression                               Number of obs   =     310259
                                                  Wald chi2(10)   =    1534.05
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood =  -2427.918                 Pseudo R2       =     0.4137

                                 (Std. Err. adjusted for 12190 clusters in ID)
------------------------------------------------------------------------------
             |               Robust
    mzfmidol |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        CIEl |  -.8937882   .1203502    -7.43   0.000     -1.12967   -.6579062
       h10dm |          0  (omitted)
      lncprt |  -.3390897   .0697503    -4.86   0.000    -.4757978   -.2023816
        mjpw |   2.232701   .2982941     7.48   0.000     1.648055    2.817347
        cntg |   1.906674   .2424967     7.86   0.000     1.431389    2.381959
        dist |   -.639605   .0969187    -6.60   0.000    -.8295622   -.4496478
    numstate |   .0038492    .002585     1.49   0.136    -.0012174    .0089158
  fatalyears |  -.7835481   .0477357   -16.41   0.000    -.8771084   -.6899877
 fatalyears2 |  -.0071252   .0005659   -12.59   0.000    -.0082344    -.006016
 fatalyears3 |   .0048165   .0004383    10.99   0.000     .0039575    .0056755
 fatalyears4 |  -.0012659   .0001605    -7.89   0.000    -.0015806   -.0009513
       _cons |    1.38344   .9947503     1.39   0.164    -.5662349    3.333115
------------------------------------------------------------------------------
(1 real change made)
specification was str4 now str5
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)

Logistic regression                               Number of obs   =     321316
                                                  Wald chi2(12)   =    2472.42
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -3974.3382                 Pseudo R2       =     0.4095

                                 (Std. Err. adjusted for 12467 clusters in ID)
------------------------------------------------------------------------------
             |               Robust
    mzmidonl |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       CIElc |   -.298252   .0621298    -4.80   0.000    -.4200242   -.1764798
       h10dm |  -.7970376   .8195485    -0.97   0.331    -2.403323    .8092479
  h10dmCIElc |  -.0045468   .2485433    -0.02   0.985    -.4916828    .4825892
      lncprt |   -.232089   .0471361    -4.92   0.000    -.3244742   -.1397039
        mjpw |   1.946759    .197999     9.83   0.000     1.558688     2.33483
        cntg |   2.787929   .2159092    12.91   0.000     2.364755    3.211103
        dist |  -.4177508   .0833243    -5.01   0.000    -.5810635   -.2544382
    numstate |   .0030071   .0018758     1.60   0.109    -.0006695    .0066836
    midyears |  -.4723852    .034624   -13.64   0.000     -.540247   -.4045233
   midyears2 |  -.0034046   .0004312    -7.90   0.000    -.0042498   -.0025595
   midyears3 |   .0017925   .0003074     5.83   0.000     .0011899     .002395
   midyears4 |   -.000405    .000119    -3.40   0.001    -.0006382   -.0001719
       _cons |  -1.767271   .7681545    -2.30   0.021    -3.272826   -.2617157
------------------------------------------------------------------------------
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)

Logistic regression                               Number of obs   =     321811
                                                  Wald chi2(12)   =    2541.38
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -5174.8452                 Pseudo R2       =     0.4544

                                 (Std. Err. adjusted for 12469 clusters in ID)
------------------------------------------------------------------------------
             |               Robust
     mzmidol |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       CIElc |  -.4387807   .0695973    -6.30   0.000    -.5751889   -.3023726
       h10dm |  -1.169065    .825025    -1.42   0.156    -2.786084    .4479542
  h10dmCIElc |   .1614878   .2430772     0.66   0.506    -.3149348    .6379104
      lncprt |  -.2577522   .0466577    -5.52   0.000    -.3491996   -.1663048
        mjpw |   2.193868   .1930911    11.36   0.000     1.815417     2.57232
        cntg |   2.160138    .189289    11.41   0.000     1.789138    2.531137
        dist |  -.5170432   .0889282    -5.81   0.000    -.6913394   -.3427471
    numstate |   .0044111   .0018403     2.40   0.017     .0008042     .008018
    midyears |  -.6822408    .035423   -19.26   0.000    -.7516687    -.612813
   midyears2 |   -.005624   .0004248   -13.24   0.000    -.0064566   -.0047913
   midyears3 |    .003117   .0002934    10.62   0.000     .0025419     .003692
   midyears4 |  -.0007401   .0001094    -6.76   0.000    -.0009546   -.0005257
       _cons |  -.2382193   .7683092    -0.31   0.757    -1.744078    1.267639
------------------------------------------------------------------------------
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
note: h10dm != 0 predicts failure perfectly
      h10dm dropped and 11309 obs not used

note: h10dmCIElc omitted because of collinearity

Logistic regression                               Number of obs   =     310000
                                                  Wald chi2(10)   =    1112.57
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -1736.8306                 Pseudo R2       =     0.2874

                                 (Std. Err. adjusted for 12188 clusters in ID)
------------------------------------------------------------------------------
             |               Robust
   mzfmidonl |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       CIElc |  -.7904656   .1068899    -7.40   0.000     -.999966   -.5809651
       h10dm |          0  (omitted)
  h10dmCIElc |          0  (omitted)
      lncprt |  -.3475746   .0685308    -5.07   0.000    -.4818925   -.2132567
        mjpw |   1.972111   .2804937     7.03   0.000     1.422353    2.521869
        cntg |   2.355697   .2730338     8.63   0.000      1.82056    2.890833
        dist |  -.6223836   .1125598    -5.53   0.000    -.8429967   -.4017706
    numstate |  -.0038161   .0026283    -1.45   0.147    -.0089675    .0013353
  fatalyears |  -.2816843    .056324    -5.00   0.000    -.3920772   -.1712914
 fatalyears2 |  -.0020118   .0006133    -3.28   0.001    -.0032137   -.0008098
 fatalyears3 |   .0014695   .0004643     3.17   0.002     .0005595    .0023795
 fatalyears4 |  -.0005347   .0001616    -3.31   0.001    -.0008514    -.000218
       _cons |  -.8458795   1.088234    -0.78   0.437     -2.97878    1.287021
------------------------------------------------------------------------------
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
note: h10dm != 0 predicts failure perfectly
      h10dm dropped and 11309 obs not used

note: h10dmCIElc omitted because of collinearity

Logistic regression                               Number of obs   =     310259
                                                  Wald chi2(10)   =    1534.05
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood =  -2427.918                 Pseudo R2       =     0.4137

                                 (Std. Err. adjusted for 12190 clusters in ID)
------------------------------------------------------------------------------
             |               Robust
    mzfmidol |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       CIElc |  -.8937882   .1203502    -7.43   0.000     -1.12967   -.6579062
       h10dm |          0  (omitted)
  h10dmCIElc |          0  (omitted)
      lncprt |  -.3390897   .0697503    -4.86   0.000    -.4757978   -.2023816
        mjpw |   2.232701   .2982941     7.48   0.000     1.648055    2.817347
        cntg |   1.906674   .2424967     7.86   0.000     1.431389    2.381959
        dist |   -.639605   .0969187    -6.60   0.000    -.8295622   -.4496478
    numstate |   .0038492    .002585     1.49   0.136    -.0012174    .0089158
  fatalyears |  -.7835481   .0477357   -16.41   0.000    -.8771084   -.6899877
 fatalyears2 |  -.0071252   .0005659   -12.59   0.000    -.0082344    -.006016
 fatalyears3 |   .0048165   .0004383    10.99   0.000     .0039575    .0056755
 fatalyears4 |  -.0012659   .0001605    -7.89   0.000    -.0015806   -.0009513
       _cons |   .0153655   .9705086     0.02   0.987    -1.886796    1.917527
------------------------------------------------------------------------------
(1 real change made)
specification was str5 now str6
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)

Logistic regression                               Number of obs   =     321316
                                                  Wald chi2(12)   =    2486.47
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -3953.6894                 Pseudo R2       =     0.4126

                                 (Std. Err. adjusted for 12467 clusters in ID)
------------------------------------------------------------------------------
             |               Robust
    mzmidonl |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        CIEl |  -.3821433   .0675346    -5.66   0.000    -.5145088   -.2497778
       h10dm |  -.7246498   .4400382    -1.65   0.100    -1.587109    .1378092
         dmh |   .0364087   .0092216     3.95   0.000     .0183347    .0544826
      lncprt |  -.2394657   .0454344    -5.27   0.000    -.3285155   -.1504159
        mjpw |   1.905546   .1881311    10.13   0.000     1.536816    2.274276
        cntg |   2.884968   .2085704    13.83   0.000     2.476178    3.293759
        dist |  -.4248543   .0768828    -5.53   0.000    -.5755418   -.2741669
    numstate |   .0019272    .001844     1.05   0.296    -.0016869    .0055414
    midyears |  -.4648186   .0342434   -13.57   0.000    -.5319345   -.3977027
   midyears2 |  -.0033991   .0004316    -7.87   0.000    -.0042451   -.0025531
   midyears3 |   .0018073   .0003083     5.86   0.000      .001203    .0024116
   midyears4 |  -.0004143   .0001194    -3.47   0.001    -.0006483   -.0001803
       _cons |  -1.205639    .733251    -1.64   0.100    -2.642785    .2315062
------------------------------------------------------------------------------
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)

Logistic regression                               Number of obs   =     321811
                                                  Wald chi2(12)   =    2634.02
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -5149.0684                 Pseudo R2       =     0.4571

                                 (Std. Err. adjusted for 12469 clusters in ID)
------------------------------------------------------------------------------
             |               Robust
     mzmidol |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        CIEl |  -.5147201   .0747067    -6.89   0.000    -.6611426   -.3682977
       h10dm |  -.6019879   .4487342    -1.34   0.180    -1.481491     .277515
         dmh |   .0359915    .009324     3.86   0.000     .0177168    .0542662
      lncprt |  -.2635434   .0448155    -5.88   0.000    -.3513801   -.1757067
        mjpw |   2.130128   .1801554    11.82   0.000      1.77703    2.483226
        cntg |   2.273807   .1858986    12.23   0.000     1.909452    2.638161
        dist |  -.5186228   .0831056    -6.24   0.000    -.6815067   -.3557388
    numstate |   .0032776   .0017972     1.82   0.068    -.0002449    .0068002
    midyears |  -.6740214    .035116   -19.19   0.000    -.7428474   -.6051953
   midyears2 |  -.0056092   .0004235   -13.25   0.000    -.0064391   -.0047792
   midyears3 |   .0031248   .0002929    10.67   0.000     .0025507    .0036988
   midyears4 |  -.0007466   .0001094    -6.82   0.000    -.0009611   -.0005322
       _cons |   .4969112   .7478702     0.66   0.506    -.9688875     1.96271
------------------------------------------------------------------------------
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
note: h10dm != 0 predicts failure perfectly
      h10dm dropped and 11309 obs not used


Logistic regression                               Number of obs   =     310000
                                                  Wald chi2(11)   =    1041.78
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -1726.7891                 Pseudo R2       =     0.2916

                                 (Std. Err. adjusted for 12188 clusters in ID)
------------------------------------------------------------------------------
             |               Robust
   mzfmidonl |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        CIEl |  -.8980804   .1044611    -8.60   0.000     -1.10282   -.6933403
       h10dm |          0  (omitted)
         dmh |   .0415813   .0142414     2.92   0.004     .0136687    .0694939
      lncprt |  -.3523567   .0660329    -5.34   0.000    -.4817788   -.2229346
        mjpw |   1.907437    .273411     6.98   0.000     1.371562    2.443313
        cntg |   2.460821   .2668409     9.22   0.000     1.937823     2.98382
        dist |  -.6276749   .1039084    -6.04   0.000    -.8313316   -.4240182
    numstate |  -.0047448    .002643    -1.80   0.073    -.0099249    .0004353
  fatalyears |  -.2679729   .0565274    -4.74   0.000    -.3787646   -.1571813
 fatalyears2 |  -.0019516   .0006191    -3.15   0.002    -.0031649   -.0007383
 fatalyears3 |   .0014492   .0004686     3.09   0.002     .0005308    .0023677
 fatalyears4 |   -.000535    .000163    -3.28   0.001    -.0008544   -.0002155
       _cons |   .4117712   1.021107     0.40   0.687    -1.589561    2.413103
------------------------------------------------------------------------------
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
note: h10dm != 0 predicts failure perfectly
      h10dm dropped and 11309 obs not used


Logistic regression                               Number of obs   =     310259
                                                  Wald chi2(11)   =    1555.81
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -2412.0918                 Pseudo R2       =     0.4176

                                 (Std. Err. adjusted for 12190 clusters in ID)
------------------------------------------------------------------------------
             |               Robust
    mzfmidol |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        CIEl |  -1.008961   .1203611    -8.38   0.000    -1.244864   -.7730576
       h10dm |          0  (omitted)
         dmh |    .042602   .0140366     3.04   0.002     .0150908    .0701133
      lncprt |  -.3483825   .0673855    -5.17   0.000    -.4804557   -.2163093
        mjpw |   2.149886   .2771936     7.76   0.000     1.606597    2.693176
        cntg |   2.050539   .2472075     8.29   0.000     1.566022    2.535057
        dist |  -.6275937   .0910376    -6.89   0.000    -.8060241   -.4491633
    numstate |   .0028425   .0026243     1.08   0.279     -.002301    .0079859
  fatalyears |  -.7725399   .0479749   -16.10   0.000     -.866569   -.6785109
 fatalyears2 |  -.0071012   .0005662   -12.54   0.000     -.008211   -.0059914
 fatalyears3 |   .0048233   .0004382    11.01   0.000     .0039645    .0056821
 fatalyears4 |  -.0012741    .000161    -7.92   0.000    -.0015896   -.0009587
       _cons |   1.318339   .9603933     1.37   0.170    -.5639968    3.200676
------------------------------------------------------------------------------
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)

Logistic regression                               Number of obs   =     321316
                                                  Wald chi2(13)   =    2515.10
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -3953.5742                 Pseudo R2       =     0.4126

                                 (Std. Err. adjusted for 12467 clusters in ID)
------------------------------------------------------------------------------
             |               Robust
    mzmidonl |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       CIElc |  -.3861904   .0693581    -5.57   0.000    -.5221298   -.2502509
       h10dm |  -.9782078   .8393615    -1.17   0.244    -2.623326    .6669105
  h10dmCIElc |   .0826681   .2568656     0.32   0.748    -.4207793    .5861155
         dmh |   .0366206    .009256     3.96   0.000     .0184792    .0547621
      lncprt |  -.2392618     .04544    -5.27   0.000    -.3283226   -.1502011
        mjpw |   1.904348   .1881301    10.12   0.000      1.53562    2.273076
        cntg |   2.884753   .2087524    13.82   0.000     2.475606      3.2939
        dist |  -.4249079   .0770072    -5.52   0.000    -.5758392   -.2739766
    numstate |   .0019313   .0018448     1.05   0.295    -.0016845    .0055471
    midyears |  -.4646908   .0342694   -13.56   0.000    -.5318576   -.3975241
   midyears2 |  -.0033987   .0004318    -7.87   0.000    -.0042449   -.0025524
   midyears3 |   .0018072   .0003084     5.86   0.000     .0012028    .0024117
   midyears4 |  -.0004143   .0001194    -3.47   0.001    -.0006483   -.0001802
       _cons |  -1.794611    .723467    -2.48   0.013     -3.21258   -.3766417
------------------------------------------------------------------------------
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)

Logistic regression                               Number of obs   =     321811
                                                  Wald chi2(13)   =    2645.51
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -5148.0074                 Pseudo R2       =     0.4572

                                 (Std. Err. adjusted for 12469 clusters in ID)
------------------------------------------------------------------------------
             |               Robust
     mzmidol |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       CIElc |  -.5264668   .0776336    -6.78   0.000    -.6786259   -.3743076
       h10dm |   -1.31966   .8397993    -1.57   0.116    -2.965636    .3263164
  h10dmCIElc |   .2475422   .2502425     0.99   0.323    -.2429241    .7380086
         dmh |   .0364834   .0093606     3.90   0.000     .0181369    .0548298
      lncprt |  -.2627871    .044812    -5.86   0.000    -.3506171   -.1749571
        mjpw |   2.126159   .1799741    11.81   0.000     1.773416    2.478902
        cntg |   2.273069   .1864124    12.19   0.000     1.907708    2.638431
        dist |  -.5187072   .0834174    -6.22   0.000    -.6822023   -.3552121
    numstate |   .0033035   .0017983     1.84   0.066     -.000221    .0068281
    midyears |  -.6737551   .0351383   -19.17   0.000     -.742625   -.6048852
   midyears2 |  -.0056087   .0004237   -13.24   0.000    -.0064391   -.0047784
   midyears3 |   .0031252    .000293    10.66   0.000     .0025508    .0036996
   midyears4 |  -.0007469   .0001095    -6.82   0.000    -.0009615   -.0005323
       _cons |   -.306127   .7343705    -0.42   0.677    -1.745467    1.133213
------------------------------------------------------------------------------
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
note: h10dm != 0 predicts failure perfectly
      h10dm dropped and 11309 obs not used

note: h10dmCIElc omitted because of collinearity

Logistic regression                               Number of obs   =     310000
                                                  Wald chi2(11)   =    1041.78
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -1726.7891                 Pseudo R2       =     0.2916

                                 (Std. Err. adjusted for 12188 clusters in ID)
------------------------------------------------------------------------------
             |               Robust
   mzfmidonl |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       CIElc |  -.8980804   .1044611    -8.60   0.000     -1.10282   -.6933403
       h10dm |          0  (omitted)
  h10dmCIElc |          0  (omitted)
         dmh |   .0415813   .0142414     2.92   0.004     .0136687    .0694939
      lncprt |  -.3523567   .0660329    -5.34   0.000    -.4817788   -.2229346
        mjpw |   1.907437    .273411     6.98   0.000     1.371562    2.443313
        cntg |   2.460821   .2668409     9.22   0.000     1.937823     2.98382
        dist |  -.6276749   .1039084    -6.04   0.000    -.8313316   -.4240182
    numstate |  -.0047448    .002643    -1.80   0.073    -.0099249    .0004353
  fatalyears |  -.2679729   .0565274    -4.74   0.000    -.3787646   -.1571813
 fatalyears2 |  -.0019516   .0006191    -3.15   0.002    -.0031649   -.0007383
 fatalyears3 |   .0014492   .0004686     3.09   0.002     .0005308    .0023677
 fatalyears4 |   -.000535    .000163    -3.28   0.001    -.0008544   -.0002155
       _cons |  -.9628729   1.014321    -0.95   0.342    -2.950906     1.02516
------------------------------------------------------------------------------
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
note: h10dm != 0 predicts failure perfectly
      h10dm dropped and 11309 obs not used

note: h10dmCIElc omitted because of collinearity

Logistic regression                               Number of obs   =     310259
                                                  Wald chi2(11)   =    1555.81
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -2412.0918                 Pseudo R2       =     0.4176

                                 (Std. Err. adjusted for 12190 clusters in ID)
------------------------------------------------------------------------------
             |               Robust
    mzfmidol |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       CIElc |  -1.008961   .1203611    -8.38   0.000    -1.244864   -.7730576
       h10dm |          0  (omitted)
  h10dmCIElc |          0  (omitted)
         dmh |    .042602   .0140366     3.04   0.002     .0150908    .0701133
      lncprt |  -.3483825   .0673855    -5.17   0.000    -.4804557   -.2163093
        mjpw |   2.149886   .2771936     7.76   0.000     1.606597    2.693176
        cntg |   2.050539   .2472075     8.29   0.000     1.566022    2.535057
        dist |  -.6275937   .0910376    -6.89   0.000    -.8060241   -.4491633
    numstate |   .0028425   .0026243     1.08   0.279     -.002301    .0079859
  fatalyears |  -.7725399   .0479749   -16.10   0.000     -.866569   -.6785109
 fatalyears2 |  -.0071012   .0005662   -12.54   0.000     -.008211   -.0059914
 fatalyears3 |   .0048233   .0004382    11.01   0.000     .0039645    .0056821
 fatalyears4 |  -.0012741    .000161    -7.92   0.000    -.0015896   -.0009587
       _cons |  -.2260238   .9467409    -0.24   0.811    -2.081602    1.629554
------------------------------------------------------------------------------
(1 real change made)
specification was str6 now str7
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)

.  
. 
. replace h10dmpvalues=2*normal(-abs(h10dmCoefficient/h10dmSE))
(8 real changes made)

. replace CIElpvalues=2*normal(-abs(CIElCoefficient/CIElSE))
(16 real changes made)

. keep specification-CIElpvalues

. drop if specnumber==.
(553771 observations deleted)

. saveold "robustnessh10dm.dta", replace
(note: file robustnessh10dm.dta not found)
file robustnessh10dm.dta saved

. 
. 
. 
. 
. 
. **Analyses with no ln(life insurance) or GDP
. 
. clear

. macro drop _all

. global filetree /Users/Allan/Dropbox/!!Papers/Liberal Peace/12-02-21_ISQ_commentary/DOR_ISQ_2013_Replication/M_R
> ep

. 
. 
. cd "$filetree"
/Users/Allan/Dropbox/!!Papers/Liberal Peace/12-02-21_ISQ_commentary/DOR_ISQ_2013_Replication/M_Rep

. 
. set more off

. 
. use "MM_precise.dta", clear

. 
. sort ccode1 ccode2 year

. gen specification="NA"

. gen specnumber=.
(553787 missing values generated)

. gen n=.
(553787 missing values generated)

. gen h10dmCoefficient=.
(553787 missing values generated)

. gen h10dmSE=.
(553787 missing values generated)

. 
. gen CIElCoefficient=.
(553787 missing values generated)

. gen CIElSE=.
(553787 missing values generated)

. gen h10dmpvalues=.
(553787 missing values generated)

. gen CIElpvalues=.
(553787 missing values generated)

. 
. sort ccode1 ccode2 year

. global controls lncprt mjpw cntg dist numstate 

. 
. global fmcontrols lncprt mjpw cntg dist numstate fpceyrs fspl1 fspl2 fspl3

. 
. global amcontrols lncprt mjpw cntg dist numstate apceyrs aspl1 aspl2 aspl3

. 
. local j=0

. 
. **M1 with 4 dependent variables x DemocracyHigh = 8 models
. local j=`j'+1

. local spec`j' "n''"

. local covars`j' "mzmidonl   h10dm                                          $controls midyears*"

. 
. local j=`j'+1

. local spec`j' "On''"

. local covars`j' "mzmidol   h10dm                                           $controls midyears*"

. 
. local j=`j'+1

. local spec`j' "Fn''"

. local covars`j' "mzfmidonl   h10dm                                         $controls fatalyears*"

. 
. local j=`j'+1

. local spec`j' "FOn''"

. local covars`j' "mzfmidol   h10dm                                          $controls fatalyears*"

. 
. 
. local j=`j'+1

. local spec`j' "Dn''"

. local covars`j' "mzmidonl   h10dm  dmh                                     $controls midyears*"

. 
. local j=`j'+1

. local spec`j' "ODn''"

. local covars`j' "mzmidol   h10dm  dmh                                      $controls midyears*"

. 
. local j=`j'+1

. local spec`j' "FDn''"

. local covars`j' "mzfmidonl   h10dm  dmh                                            $controls fatalyears*"

. 
. local j=`j'+1

. local spec`j' "FODn''"

. local covars`j' "mzfmidol   h10dm  dmh                                     $controls fatalyears*"

. 
. 
. 
. **Estimating Models, Saving Values
. forvalues k=1(1)`j' {
  2. logit `covars`k'', cl(ID) nolog
  3. replace n= e(N)   if _n==`k'
  4. replace specification="`spec`k'''" if _n==`k'
  5. replace specnumber=`k' if _n==`k'
  6. replace h10dmCoefficient=_b[h10dm] if _n==`k'
  7. replace h10dmSE=_se[h10dm] if _n==`k'
  8. *replace CIElCoefficient=_b[CIEl] if _n==`k'
. *replace CIElSE=_se[CIEl] if _n==`k'
. }

Logistic regression                               Number of obs   =     391599
                                                  Wald chi2(10)   =    2916.48
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -4861.1037                 Pseudo R2       =     0.3945

                                 (Std. Err. adjusted for 13538 clusters in ID)
------------------------------------------------------------------------------
             |               Robust
    mzmidonl |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       h10dm |  -1.942409   .3724041    -5.22   0.000    -2.672307    -1.21251
      lncprt |  -.1994238   .0402037    -4.96   0.000    -.2782216   -.1206261
        mjpw |   1.790582   .1742453    10.28   0.000     1.449068    2.132097
        cntg |    2.99151   .1912532    15.64   0.000     2.616661     3.36636
        dist |  -.3646836    .063395    -5.75   0.000    -.4889355   -.2404316
    numstate |   .0026942   .0017111     1.57   0.115    -.0006594    .0060478
    midyears |   -.443717   .0306657   -14.47   0.000    -.5038207   -.3836133
   midyears2 |  -.0032544    .000389    -8.37   0.000    -.0040168    -.002492
   midyears3 |   .0017643   .0002831     6.23   0.000     .0012095    .0023191
   midyears4 |   -.000423    .000113    -3.74   0.000    -.0006445   -.0002015
       _cons |  -2.191071   .6336071    -3.46   0.001    -3.432918   -.9492236
------------------------------------------------------------------------------
(1 real change made)
specification was str2 now str4
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)

Logistic regression                               Number of obs   =     392172
                                                  Wald chi2(10)   =    2840.72
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -6424.3564                 Pseudo R2       =     0.4281

                                 (Std. Err. adjusted for 13538 clusters in ID)
------------------------------------------------------------------------------
             |               Robust
     mzmidol |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       h10dm |  -2.297649   .3655862    -6.28   0.000    -3.014185   -1.581114
      lncprt |  -.2292374   .0390697    -5.87   0.000    -.3058127   -.1526622
        mjpw |   2.031121   .1745138    11.64   0.000      1.68908    2.373162
        cntg |   2.411764   .1751451    13.77   0.000     2.068486    2.755042
        dist |  -.4501611   .0686137    -6.56   0.000    -.5846415   -.3156808
    numstate |   .0032892   .0016931     1.94   0.052    -.0000292    .0066076
    midyears |  -.6390598   .0317022   -20.16   0.000     -.701195   -.5769246
   midyears2 |  -.0053296   .0003839   -13.88   0.000    -.0060821   -.0045771
   midyears3 |   .0030057   .0002698    11.14   0.000     .0024768    .0035346
   midyears4 |  -.0007373   .0001038    -7.10   0.000    -.0009408   -.0005338
       _cons |  -.5735877   .6479395    -0.89   0.376    -1.843526    .6963505
------------------------------------------------------------------------------
(1 real change made)
specification was str4 now str5
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
note: h10dm != 0 predicts failure perfectly
      h10dm dropped and 11364 obs not used


Logistic regression                               Number of obs   =     380186
                                                  Wald chi2(9)    =     956.70
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -2139.6282                 Pseudo R2       =     0.2571

                                 (Std. Err. adjusted for 13259 clusters in ID)
------------------------------------------------------------------------------
             |               Robust
   mzfmidonl |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       h10dm |          0  (omitted)
      lncprt |  -.2807506    .056349    -4.98   0.000    -.3911926   -.1703086
        mjpw |   1.517946   .2623964     5.78   0.000     1.003658    2.032233
        cntg |   2.590394   .2617633     9.90   0.000     2.077348    3.103441
        dist |  -.5032865   .0942657    -5.34   0.000    -.6880438   -.3185291
    numstate |  -.0021547   .0025136    -0.86   0.391    -.0070813    .0027718
  fatalyears |  -.2988069   .0480953    -6.21   0.000     -.393072   -.2045417
 fatalyears2 |  -.0023155   .0005467    -4.24   0.000    -.0033871   -.0012439
 fatalyears3 |   .0016805   .0004229     3.97   0.000     .0008517    .0025093
 fatalyears4 |  -.0005674   .0001507    -3.77   0.000    -.0008627   -.0002721
       _cons |  -1.479663   .9802162    -1.51   0.131    -3.400852    .4415253
------------------------------------------------------------------------------
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
note: h10dm != 0 predicts failure perfectly
      h10dm dropped and 11364 obs not used


Logistic regression                               Number of obs   =     380473
                                                  Wald chi2(9)    =    1351.52
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -3011.7221                 Pseudo R2       =     0.3706

                                 (Std. Err. adjusted for 13259 clusters in ID)
------------------------------------------------------------------------------
             |               Robust
    mzfmidol |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       h10dm |          0  (omitted)
      lncprt |  -.3405345    .058169    -5.85   0.000    -.4545436   -.2265255
        mjpw |    1.92629   .2806421     6.86   0.000     1.376242    2.476338
        cntg |   2.301255   .2468844     9.32   0.000     1.817371     2.78514
        dist |  -.5072512   .0882516    -5.75   0.000    -.6802211   -.3342813
    numstate |   .0023989   .0025704     0.93   0.351     -.002639    .0074368
  fatalyears |  -.7410656    .042822   -17.31   0.000    -.8249951   -.6571361
 fatalyears2 |   -.006805   .0005295   -12.85   0.000    -.0078429   -.0057672
 fatalyears3 |   .0046143   .0004167    11.07   0.000     .0037975     .005431
 fatalyears4 |  -.0012063   .0001535    -7.86   0.000    -.0015073   -.0009054
       _cons |  -.3491473   .9151404    -0.38   0.703    -2.142789    1.444495
------------------------------------------------------------------------------
(1 real change made)
specification was str5 now str6
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)

Logistic regression                               Number of obs   =     391599
                                                  Wald chi2(11)   =    2959.49
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood =  -4855.184                 Pseudo R2       =     0.3952

                                 (Std. Err. adjusted for 13538 clusters in ID)
------------------------------------------------------------------------------
             |               Robust
    mzmidonl |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       h10dm |  -2.030035   .3748541    -5.42   0.000    -2.764736   -1.295334
         dmh |   .0164725   .0077657     2.12   0.034     .0012521    .0316929
      lncprt |  -.1988929   .0392949    -5.06   0.000    -.2759095   -.1218763
        mjpw |   1.733731   .1709851    10.14   0.000     1.398607    2.068856
        cntg |   3.039931   .1895871    16.03   0.000     2.668347    3.411515
        dist |  -.3595435   .0610874    -5.89   0.000    -.4792725   -.2398144
    numstate |   .0020051   .0017476     1.15   0.251    -.0014201    .0054303
    midyears |  -.4406562   .0304914   -14.45   0.000    -.5004183   -.3808941
   midyears2 |  -.0032448   .0003889    -8.34   0.000    -.0040071   -.0024826
   midyears3 |   .0017658   .0002835     6.23   0.000     .0012101    .0023215
   midyears4 |  -.0004254   .0001133    -3.75   0.000    -.0006474   -.0002033
       _cons |  -2.190795   .6171857    -3.55   0.000    -3.400457   -.9811332
------------------------------------------------------------------------------
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)

Logistic regression                               Number of obs   =     392172
                                                  Wald chi2(11)   =    2868.88
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood =  -6420.455                 Pseudo R2       =     0.4285

                                 (Std. Err. adjusted for 13538 clusters in ID)
------------------------------------------------------------------------------
             |               Robust
     mzmidol |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       h10dm |  -2.356968   .3678012    -6.41   0.000    -3.077845   -1.636091
         dmh |   .0117102   .0075775     1.55   0.122    -.0031414    .0265617
      lncprt |  -.2292447   .0385455    -5.95   0.000    -.3047924    -.153697
        mjpw |   1.988805   .1707559    11.65   0.000      1.65413    2.323481
        cntg |      2.448   .1752531    13.97   0.000     2.104511     2.79149
        dist |  -.4459811   .0669159    -6.66   0.000    -.5771337   -.3148284
    numstate |   .0027858   .0017179     1.62   0.105    -.0005812    .0061528
    midyears |  -.6368323   .0316398   -20.13   0.000    -.6988452   -.5748194
   midyears2 |  -.0053227   .0003839   -13.87   0.000    -.0060751   -.0045703
   midyears3 |   .0030068     .00027    11.13   0.000     .0024776    .0035361
   midyears4 |  -.0007392    .000104    -7.11   0.000    -.0009429   -.0005354
       _cons |  -.5741646   .6342073    -0.91   0.365    -1.817188    .6688588
------------------------------------------------------------------------------
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
note: h10dm != 0 predicts failure perfectly
      h10dm dropped and 11364 obs not used


Logistic regression                               Number of obs   =     380186
                                                  Wald chi2(10)   =     965.01
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -2139.1575                 Pseudo R2       =     0.2573

                                 (Std. Err. adjusted for 13259 clusters in ID)
------------------------------------------------------------------------------
             |               Robust
   mzfmidonl |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       h10dm |          0  (omitted)
         dmh |    .007922   .0131478     0.60   0.547    -.0178473    .0336912
      lncprt |   -.280466   .0562259    -4.99   0.000    -.3906667   -.1702653
        mjpw |   1.487143   .2670311     5.57   0.000     .9637714    2.010514
        cntg |   2.611995   .2642902     9.88   0.000     2.093996    3.129994
        dist |   -.500156   .0930521    -5.38   0.000    -.6825347   -.3177773
    numstate |  -.0024597   .0026346    -0.93   0.350    -.0076234    .0027039
  fatalyears |  -.2963417   .0481481    -6.15   0.000    -.3907101   -.2019732
 fatalyears2 |  -.0023007   .0005485    -4.19   0.000    -.0033758   -.0012256
 fatalyears3 |   .0016738   .0004239     3.95   0.000     .0008431    .0025046
 fatalyears4 |  -.0005666   .0001509    -3.76   0.000    -.0008624   -.0002709
       _cons |  -1.487795    .969381    -1.53   0.125    -3.387747    .4121567
------------------------------------------------------------------------------
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
note: h10dm != 0 predicts failure perfectly
      h10dm dropped and 11364 obs not used


Logistic regression                               Number of obs   =     380473
                                                  Wald chi2(10)   =    1349.56
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -3011.3343                 Pseudo R2       =     0.3707

                                 (Std. Err. adjusted for 13259 clusters in ID)
------------------------------------------------------------------------------
             |               Robust
    mzfmidol |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       h10dm |          0  (omitted)
         dmh |    .005797   .0124917     0.46   0.643    -.0186863    .0302804
      lncprt |  -.3409272   .0578612    -5.89   0.000    -.4543331   -.2275213
        mjpw |   1.905291   .2748481     6.93   0.000     1.366599    2.443983
        cntg |   2.321454   .2519517     9.21   0.000     1.827638     2.81527
        dist |  -.5028748   .0891481    -5.64   0.000     -.677602   -.3281477
    numstate |   .0021517   .0027005     0.80   0.426    -.0031413    .0074446
  fatalyears |  -.7397017   .0431448   -17.14   0.000    -.8242639   -.6551395
 fatalyears2 |   -.006799   .0005311   -12.80   0.000    -.0078399   -.0057581
 fatalyears3 |   .0046127   .0004171    11.06   0.000     .0037953    .0054301
 fatalyears4 |  -.0012066   .0001536    -7.86   0.000    -.0015077   -.0009056
       _cons |  -.3665485    .911534    -0.40   0.688    -2.153122    1.420025
------------------------------------------------------------------------------
(1 real change made)
specification was str6 now str7
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)

.  
. replace h10dmpvalues=2*normal(-abs(h10dmCoefficient/h10dmSE))
(4 real changes made)

. replace CIElpvalues=2*normal(-abs(CIElCoefficient/CIElSE))
(0 real changes made)

. keep specification-CIElpvalues

. drop if specnumber==.
(553779 observations deleted)

. saveold "robustnessh10dm2.dta", replace
(note: file robustnessh10dm2.dta not found)
file robustnessh10dm2.dta saved

. 
. 
end of do-file

. *This produces robustnessh10dm.dta and robustnessh10dm2.dta
. 
. ***Running robustness specifications on Multiply Imputed data.***
. clear

. do "12-09-24_loop_mi_robustness.do"

. *12-09-24_loop_mi_robustness.do
. 
. 
. clear

. macro drop _all

. global filetree /Users/Allan/Dropbox/!!Papers/Liberal Peace/12-02-21_ISQ_commentary/DOR_ISQ_2013_Replication/M_R
> ep

. 
. cd "$filetree"
/Users/Allan/Dropbox/!!Papers/Liberal Peace/12-02-21_ISQ_commentary/DOR_ISQ_2013_Replication/M_Rep

. use "midata"

. 
. mi xtset, clear

. 
. **check that logit runs with midyears*
. sort ccode1 ccode2 year

. global controls lncprt mjpw contigl lndist numstate

. 
. gen specification="NA"

. gen specnumber=.
(436541 missing values generated)

. gen n=.
(436541 missing values generated)

. gen DmlCoefficient=.
(436541 missing values generated)

. gen DmlSE=.
(436541 missing values generated)

. gen CIElCoefficient=.
(436541 missing values generated)

. gen CIElSE=.
(436541 missing values generated)

. gen Dmlpvalues=.
(436541 missing values generated)

. gen CIElpvalues=.
(436541 missing values generated)

. 
. sum _1_lnlifepenl, d

                        _1_lnlifepenl
-------------------------------------------------------------
      Percentiles      Smallest
 1%    -10.19686      -11.70734
 5%    -7.581344      -11.70734
10%     -7.06207      -11.70734       Obs              436541
25%    -6.542002      -11.70734       Sum of Wgt.      436541

50%    -6.116163                      Mean          -6.124856
                        Largest       Std. Dev.      .9655209
75%    -5.613044      -2.299634
90%    -5.082771      -2.299634       Variance       .9322306
95%    -4.682591      -2.299634       Skewness      -1.155934
99%    -3.823621      -2.299634       Kurtosis       8.466496

. local p75=r(p75)

. mi passive: generate lnlifepenlc=lnlifepenl-`p75'
m=0:
(398924 missing values generated)
m=1:
m=2:
m=3:
m=4:
m=5:
m=6:
m=7:
m=8:
m=9:
m=10:
m=11:
m=12:
m=13:
m=14:
m=15:
m=16:
m=17:
m=18:
m=19:
m=20:

. mi passive: generate dmllnlifepenlc=dml*lnlifepenlc
m=0:
(398924 missing values generated)
m=1:
m=2:
m=3:
m=4:
m=5:
m=6:
m=7:
m=8:
m=9:
m=10:
m=11:
m=12:
m=13:
m=14:
m=15:
m=16:
m=17:
m=18:
m=19:
m=20:

. 
. ***Code just to see if things are running
. *mi estimate, dots: logit fmidonsl lnlifedeerl dml $controls fmidyears*, cl(dyadid) 
. *logit midonsl _1_lnlifedeerl dml $controls      midyears*, cl(dyadid) 
. *logit midonsl _2_lnlifedeerl dml $controls      midyears*, cl(dyadid) 
. *logit fmidonsl _3_lnlifedeerl dml $controls     fmidyears*, cl(dyadid) nolog
. *****
. 
. sort ccode1 ccode2 year

. 
. local j=0

. 
. **M2 m.i. with 4 dependent variables x interaction = 8 models
. local j=`j'+1

. local spec`j' "M"

. local covars`j' "midonsl CIEl  dml                                         $controls midyears*"

. 
. 
. local j=`j'+1

. local spec`j' "OM"

. local covars`j' "midongl CIEl  dml                                         $controls midyears*"

. 
. 
. local j=`j'+1

. local spec`j' "FM"

. local covars`j' "fmidonsl CIEl  dml                                        $controls fmidyears*"

. 
. 
. local j=`j'+1

. local spec`j' "FOM"

. local covars`j' "fmidongl CIEl  dml                                        $controls fmidyears*"

. 
. local j=`j'+1

. local spec`j' "IM"

. local covars`j' "midonsl CIElc  dml dmlCIElc                                       $controls midyears*"

. 
. 
. local j=`j'+1

. local spec`j' "OIM"

. local covars`j' "midongl CIElc  dml      dmlCIElc                                  $controls midyears*"

. 
. 
. local j=`j'+1

. local spec`j' "FIM"

. local covars`j' "fmidonsl CIElc  dml             dmlCIElc                          $controls fmidyears*"

. 
. 
. local j=`j'+1

. local spec`j' "FOIM"

. local covars`j' "fmidongl CIElc  dml    dmlCIElc                                   $controls fmidyears*"

. 
. 
. 
. **M2 m.i. with DemocracyHigh with 4 dependent variables x interaction = 8 models
. 
. 
. **M2 with 4 dependent variables x interaction = 8 models
. local j=`j'+1

. local spec`j' "DM"

. local covars`j' "midonsl CIEl  dml  dmh                                            $controls midyears*"

. 
. 
. local j=`j'+1

. local spec`j' "ODM"

. local covars`j' "midongl CIEl  dml  dmh                                            $controls midyears*"

. 
. 
. local j=`j'+1

. local spec`j' "FDM"

. local covars`j' "fmidonsl CIEl  dml  dmh                                           $controls fmidyears*"

. 
. 
. local j=`j'+1

. local spec`j' "FODM"

. local covars`j'  "fmidongl CIEl  dml  dmh                                          $controls fmidyears*"

. 
. 
. local j=`j'+1

. local spec`j'  "IDM"

. local covars`j'  "midonsl CIElc  dml dmlCIElc    dmh                               $controls midyears*"

. 
. 
. local j=`j'+1

. local spec`j'  "OIDM"

. local covars`j'  "midongl CIElc  dml     dmlCIElc        dmh                       $controls midyears*"

. 
. 
. local j=`j'+1

. local spec`j'  "FIDM"

. local covars`j'  "fmidonsl CIElc  dml            dmlCIElc        dmh               $controls fmidyears*"

. 
. 
. local j=`j'+1

. local spec`j'  "FOIDM"

. local covars`j' "fmidongl CIElc  dml    dmlCIElc         dmh                       $controls fmidyears*"

. 
. 
. 
. 
. ****With other measures of life insurance expenditures
. 
. 
. **M2 m.i. with 4 dependent variables x interaction = 8 models
. local j=`j'+1

. local spec`j' "L"

. local covars`j' "midonsl lnlifepenlc  dml                                          $controls midyears*"

. 
. 
. local j=`j'+1

. local spec`j' "OL"

. local covars`j' "midongl lnlifepenlc  dml                                          $controls midyears*"

. 
. 
. local j=`j'+1

. local spec`j' "FL"

. local covars`j' "fmidonsl lnlifepenlc  dml                                         $controls fmidyears*"

. 
. 
. local j=`j'+1

. local spec`j' "FOL"

. local covars`j' "fmidongl lnlifepenlc  dml                                         $controls fmidyears*"

. 
. local j=`j'+1

. local spec`j' "IL"

. local covars`j' "midonsl lnlifepenlc  dml dmllnlifepenlc                                           $controls mid
> years*"

. 
. 
. local j=`j'+1

. local spec`j' "OIL"

. local covars`j' "midongl lnlifepenlc  dml        dmllnlifepenlc                                    $controls mid
> years*"

. 
. 
. local j=`j'+1

. local spec`j' "FIL"

. local covars`j' "fmidonsl lnlifepenlc  dml               dmllnlifepenlc                            $controls fmi
> dyears*"

. 
. 
. local j=`j'+1

. local spec`j' "FOIL"

. local covars`j' "fmidongl lnlifepenlc  dml      dmllnlifepenlc                             $controls fmidyears*"

. 
. 
. 
. **M2 m.i. with DemocracyHigh with 4 dependent variables x interaction = 8 models
. 
. local j=`j'+1

. local spec`j' "DL"

. local covars`j' "midonsl lnlifepenlc  dml  dmh                                     $controls midyears*"

. 
. 
. local j=`j'+1

. local spec`j' "ODL"

. local covars`j' "midongl lnlifepenlc  dml  dmh                                     $controls midyears*"

. 
. 
. local j=`j'+1

. local spec`j' "FDL"

. local covars`j' "fmidonsl lnlifepenlc  dml  dmh                                            $controls fmidyears*"

. 
. 
. local j=`j'+1

. local spec`j' "FODL"

. local covars`j'  "fmidongl lnlifepenlc  dml  dmh                                           $controls fmidyears*"

. 
. 
. local j=`j'+1

. local spec`j'  "IDL"

. local covars`j'  "midonsl lnlifepenlc  dml dmllnlifepenlc        dmh                               $controls mid
> years*"

. 
. 
. local j=`j'+1

. local spec`j'  "OIDL"

. local covars`j'  "midongl lnlifepenlc  dml       dmllnlifepenlc          dmh                       $controls mid
> years*"

. 
. 
. local j=`j'+1

. local spec`j'  "FIDL"

. local covars`j'  "fmidonsl lnlifepenlc  dml              dmllnlifepenlc          dmh               $controls fmi
> dyears*"

. 
. 
. local j=`j'+1

. local spec`j'  "FOIDL"

. local covars`j' "fmidongl lnlifepenlc  dml      dmllnlifepenlc   dmh                       $controls fmidyears*"

. 
. 
. sort ccode1 ccode2 year

. 
. **Estimating Models, Saving Values
. forvalues k=1(1)`j' {
  2. replace specification="`spec`k''" if _n==`k'
  3. mi estimate, dots: logit `covars`k'', cl(dyadid) nolog
  4. replace n= e(N)   if _n==`k'
  5. mat A=e(b_mi)
  6. mat V=e(V_mi)
  7. replace specnumber=`k' if _n==`k'
  8. replace DmlCoefficient=A[1,2] if _n==`k'
  9. replace DmlSE=(V[2,2])^(1/2) if _n==`k'
 10. replace CIElCoefficient=A[1,1] if _n==`k'
 11. replace CIElSE=(V[1,1])^(1/2) if _n==`k'
 12. }
(1 real change made)

Imputations (20):
  .........10.........20 done

Multiple-imputation estimates                     Imputations     =         20
Logistic regression                               Number of obs   =     423001
                                                  Average RVI     =     0.0241
                                                  Largest FMI     =     0.1581
DF adjustment:   Large sample                     DF:     min     =     780.86
                                                          avg     =   2.12e+08
                                                          max     =   2.23e+09
Model F test:       Equal FMI                     F(  10,343423.0)=     192.04
Within VCE type:       Robust                     Prob > F        =     0.0000

                            (Within VCE adjusted for 13538 clusters in dyadid)
------------------------------------------------------------------------------
     midonsl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        CIEl |  -.1073495   .0578996    -1.85   0.064    -.2210069    .0063079
         dml |  -.0510159   .0137463    -3.71   0.000    -.0779593   -.0240725
      lncprt |  -.2308394   .0620752    -3.72   0.000    -.3525045   -.1091742
        mjpw |   1.988765   .2492342     7.98   0.000     1.500275    2.477255
     contigl |  -4.348405   .7418121    -5.86   0.000    -5.802332   -2.894479
      lndist |  -.9630441   .1014702    -9.49   0.000    -1.161922   -.7641661
    numstate |   .0071911   .0013332     5.39   0.000     .0045779    .0098042
    midyears |  -.4209083   .0282234   -14.91   0.000     -.476225   -.3655915
   midyears2 |   .0165068   .0015531    10.63   0.000     .0134628    .0195507
   midyears3 |   -.000187   .0000235    -7.95   0.000    -.0002331   -.0001409
       _cons |   2.131956    .781712     2.73   0.006     .5998264    3.664085
------------------------------------------------------------------------------
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)

Imputations (20):
  .........10.........20 done

Multiple-imputation estimates                     Imputations     =         20
Logistic regression                               Number of obs   =     423726
                                                  Average RVI     =     0.0340
                                                  Largest FMI     =     0.2169
DF adjustment:   Large sample                     DF:     min     =     418.23
                                                          avg     =   1.21e+08
                                                          max     =   1.19e+09
Model F test:       Equal FMI                     F(  10,169741.3)=     193.67
Within VCE type:       Robust                     Prob > F        =     0.0000

                            (Within VCE adjusted for 13538 clusters in dyadid)
------------------------------------------------------------------------------
     midongl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        CIEl |  -.1084892   .0560246    -1.94   0.053     -.218614    .0016357
         dml |  -.0722912   .0131982    -5.48   0.000    -.0981603    -.046422
      lncprt |  -.2611569   .0526544    -4.96   0.000    -.3643576   -.1579562
        mjpw |    2.20581     .22393     9.85   0.000     1.766915    2.644705
     contigl |  -3.493246   .7129454    -4.90   0.000    -4.890596   -2.095897
      lndist |  -.8165033   .0944823    -8.64   0.000    -1.001685   -.6313212
    numstate |   .0079163   .0012087     6.55   0.000     .0055469    .0102856
    midyears |  -.5942611   .0279255   -21.28   0.000     -.648994   -.5395282
   midyears2 |   .0251853   .0015626    16.12   0.000     .0221226     .028248
   midyears3 |  -.0003027   .0000242   -12.50   0.000    -.0003501   -.0002552
       _cons |   1.663877   .7388248     2.25   0.024     .2158046     3.11195
------------------------------------------------------------------------------
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)

Imputations (20):
  .........10.........20 done

Multiple-imputation estimates                     Imputations     =         20
Logistic regression                               Number of obs   =     422735
                                                  Average RVI     =     0.0448
                                                  Largest FMI     =     0.1766
DF adjustment:   Large sample                     DF:     min     =     627.74
                                                          avg     =   7.21e+07
                                                          max     =   4.95e+08
Model F test:       Equal FMI                     F(  10,130281.6)=      70.18
Within VCE type:       Robust                     Prob > F        =     0.0000

                            (Within VCE adjusted for 13538 clusters in dyadid)
------------------------------------------------------------------------------
    fmidonsl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        CIEl |  -.4662798   .1567848    -2.97   0.003    -.7741661   -.1583936
         dml |  -.0338618   .0361672    -0.94   0.349    -.1047492    .0370256
      lncprt |   -.355025   .1149118    -3.09   0.002    -.5802481    -.129802
        mjpw |    1.55717   .4872297     3.20   0.001      .602217    2.512124
     contigl |   3.058437   2.869859     1.07   0.287    -2.566384    8.683258
      lndist |  -.1904034    .364595    -0.52   0.602    -.9049965    .5241897
    numstate |   .0122429   .0035038     3.49   0.000      .005374    .0191119
   fmidyears |  -.4602471   .0748956    -6.15   0.000    -.6070398   -.3134544
  fmidyears2 |    .015671   .0042707     3.67   0.000     .0073006    .0240414
  fmidyears3 |  -.0001493   .0000646    -2.31   0.021    -.0002758   -.0000227
       _cons |  -6.692689   2.832752    -2.36   0.018    -12.24478   -1.140597
------------------------------------------------------------------------------
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
specification was str2 now str3
(1 real change made)

Imputations (20):
  .........10.........20 done

Multiple-imputation estimates                     Imputations     =         20
Logistic regression                               Number of obs   =     423430
                                                  Average RVI     =     0.0938
                                                  Largest FMI     =     0.3360
DF adjustment:   Large sample                     DF:     min     =     176.12
                                                          avg     = 9570340.34
                                                          max     =   3.66e+07
Model F test:       Equal FMI                     F(  10,28376.5) =      63.73
Within VCE type:       Robust                     Prob > F        =     0.0000

                            (Within VCE adjusted for 13538 clusters in dyadid)
------------------------------------------------------------------------------
    fmidongl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        CIEl |  -.3436629   .0957231    -3.59   0.000    -.5325748   -.1547509
         dml |  -.0823304    .025559    -3.22   0.001    -.1324266   -.0322341
      lncprt |  -.3633117   .0785931    -4.62   0.000    -.5173514   -.2092721
        mjpw |   2.222726   .3687516     6.03   0.000     1.499986    2.945466
     contigl |  -1.164154   1.387607    -0.84   0.401    -3.883815    1.555507
      lndist |  -.5503761   .1774395    -3.10   0.002    -.8981512   -.2026009
    numstate |   .0061825   .0020588     3.00   0.003      .002145    .0102199
   fmidyears |   -.668485   .0446857   -14.96   0.000    -.7560674   -.5809027
  fmidyears2 |   .0272297   .0022795    11.95   0.000      .022762    .0316974
  fmidyears3 |  -.0003035   .0000324    -9.37   0.000     -.000367     -.00024
       _cons |  -.7427866   1.412276    -0.53   0.599    -3.510798    2.025225
------------------------------------------------------------------------------
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)

Imputations (20):
  .........10.........20 done

Multiple-imputation estimates                     Imputations     =         20
Logistic regression                               Number of obs   =     423001
                                                  Average RVI     =     0.0281
                                                  Largest FMI     =     0.0959
DF adjustment:   Large sample                     DF:     min     =    2102.87
                                                          avg     =   6.28e+07
                                                          max     =   3.22e+08
Model F test:       Equal FMI                     F(  11,232900.1)=     167.74
Within VCE type:       Robust                     Prob > F        =     0.0000

                            (Within VCE adjusted for 13538 clusters in dyadid)
------------------------------------------------------------------------------
     midonsl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       CIElc |  -.2057059   .0620457    -3.32   0.001    -.3273832   -.0840286
         dml |  -.0475781   .0108804    -4.37   0.000    -.0689054   -.0262508
    dmlCIElc |  -.0296646    .006497    -4.57   0.000    -.0424018   -.0169274
      lncprt |  -.2474286   .0613355    -4.03   0.000     -.367644   -.1272132
        mjpw |    2.11506   .2421913     8.73   0.000     1.640374    2.589747
     contigl |  -4.376665   .7430223    -5.89   0.000    -5.832964   -2.920366
      lndist |  -.9748017   .1010274    -9.65   0.000    -1.172812   -.7767914
    numstate |   .0065489   .0013356     4.90   0.000      .003931    .0091668
    midyears |  -.4151205   .0280286   -14.81   0.000    -.4700555   -.3601855
   midyears2 |   .0161281   .0015424    10.46   0.000     .0131051    .0191511
   midyears3 |  -.0001809   .0000234    -7.74   0.000    -.0002268   -.0001351
       _cons |   2.168394   .7606702     2.85   0.004      .677508    3.659281
------------------------------------------------------------------------------
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)

Imputations (20):
  .........10.........20 done

Multiple-imputation estimates                     Imputations     =         20
Logistic regression                               Number of obs   =     423726
                                                  Average RVI     =     0.0430
                                                  Largest FMI     =     0.1230
DF adjustment:   Large sample                     DF:     min     =    1284.39
                                                          avg     =   8.67e+07
                                                          max     =   3.89e+08
Model F test:       Equal FMI                     F(  11,103591.7)=     172.04
Within VCE type:       Robust                     Prob > F        =     0.0000

                            (Within VCE adjusted for 13538 clusters in dyadid)
------------------------------------------------------------------------------
     midongl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       CIElc |  -.2140299   .0601524    -3.56   0.000    -.3320377   -.0960221
         dml |  -.0710306   .0109467    -6.49   0.000    -.0924873   -.0495739
    dmlCIElc |  -.0267187   .0061332    -4.36   0.000     -.038748   -.0146894
      lncprt |  -.2727741   .0518851    -5.26   0.000     -.374467   -.1710811
        mjpw |   2.299755     .21764    10.57   0.000     1.873189    2.726322
     contigl |  -3.525724   .7178485    -4.91   0.000    -4.932683   -2.118765
      lndist |  -.8270174   .0947954    -8.72   0.000    -1.012813   -.6412218
    numstate |   .0074504   .0012112     6.15   0.000     .0050758    .0098251
    midyears |  -.5893072   .0279103   -21.11   0.000    -.6440103   -.5346041
   midyears2 |   .0248506   .0015607    15.92   0.000     .0217917    .0279095
   midyears3 |  -.0002973   .0000242   -12.28   0.000    -.0003447   -.0002499
       _cons |   1.637571   .7299432     2.24   0.025     .2069081    3.068233
------------------------------------------------------------------------------
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Imputations (20):
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Multiple-imputation estimates                     Imputations     =         20
Logistic regression                               Number of obs   =     422735
                                                  Average RVI     =     0.0594
                                                  Largest FMI     =     0.1814
DF adjustment:   Large sample                     DF:     min     =     595.57
                                                          avg     =   5.92e+07
                                                          max     =   4.12e+08
Model F test:       Equal FMI                     F(  11,66715.1) =      60.02
Within VCE type:       Robust                     Prob > F        =     0.0000

                            (Within VCE adjusted for 13538 clusters in dyadid)
------------------------------------------------------------------------------
    fmidonsl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       CIElc |   -.700728   .1469879    -4.77   0.000    -.9894028   -.4120533
         dml |  -.0803763   .0288841    -2.78   0.005     -.136998   -.0237545
    dmlCIElc |  -.0558663   .0138864    -4.02   0.000    -.0831386   -.0285941
      lncprt |  -.3741704   .1169499    -3.20   0.001    -.6033883   -.1449526
        mjpw |   1.772502   .4864577     3.64   0.000     .8190614    2.725943
     contigl |   2.993031   2.952437     1.01   0.311    -2.793638    8.779701
      lndist |  -.2043738   .3745376    -0.55   0.585     -.938454    .5297064
    numstate |   .0124683   .0035145     3.55   0.000     .0055782    .0193584
   fmidyears |  -.4571496   .0748089    -6.11   0.000    -.6037724   -.3105267
  fmidyears2 |   .0152989   .0042797     3.57   0.000     .0069109    .0236868
  fmidyears3 |  -.0001428   .0000649    -2.20   0.028    -.0002699   -.0000156
       _cons |  -7.770022   2.908254    -2.67   0.008     -13.4701   -2.069942
------------------------------------------------------------------------------
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Imputations (20):
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Multiple-imputation estimates                     Imputations     =         20
Logistic regression                               Number of obs   =     423430
                                                  Average RVI     =     0.1089
                                                  Largest FMI     =     0.2585
DF adjustment:   Large sample                     DF:     min     =     295.62
                                                          avg     =   1.16e+07
                                                          max     =   7.22e+07
Model F test:       Equal FMI                     F(  11,20913.9) =      58.75
Within VCE type:       Robust                     Prob > F        =     0.0000

                            (Within VCE adjusted for 13538 clusters in dyadid)
------------------------------------------------------------------------------
    fmidongl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       CIElc |  -.5629125   .0947391    -5.94   0.000    -.7493611   -.3764639
         dml |  -.1135737    .022465    -5.06   0.000    -.1576081   -.0695392
    dmlCIElc |  -.0440058   .0095847    -4.59   0.000    -.0628537   -.0251578
      lncprt |  -.3723743   .0785109    -4.74   0.000    -.5262528   -.2184958
        mjpw |   2.345486   .3569097     6.57   0.000     1.645956    3.045016
     contigl |  -1.250843   1.414143    -0.88   0.376    -4.022513    1.520828
      lndist |  -.5655185    .180477    -3.13   0.002     -.919247     -.21179
    numstate |   .0063501   .0020854     3.05   0.002     .0022602    .0104401
   fmidyears |  -.6638756   .0448909   -14.79   0.000    -.7518601   -.5758911
  fmidyears2 |   .0268246   .0022996    11.67   0.000     .0223175    .0313316
  fmidyears3 |  -.0002968   .0000327    -9.07   0.000     -.000361   -.0002327
       _cons |  -1.515885   1.426501    -1.06   0.288    -4.311782    1.280011
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Imputations (20):
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Multiple-imputation estimates                     Imputations     =         20
Logistic regression                               Number of obs   =     423001
                                                  Average RVI     =     0.0234
                                                  Largest FMI     =     0.1494
DF adjustment:   Large sample                     DF:     min     =     873.60
                                                          avg     =   1.26e+08
                                                          max     =   1.43e+09
Model F test:       Equal FMI                     F(  11,508945.3)=     177.87
Within VCE type:       Robust                     Prob > F        =     0.0000

                            (Within VCE adjusted for 13538 clusters in dyadid)
------------------------------------------------------------------------------
     midonsl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        CIEl |  -.1492078     .05692    -2.62   0.009    -.2609237   -.0374918
         dml |  -.0828222   .0127306    -6.51   0.000    -.1077742   -.0578701
         dmh |   .0640488   .0096222     6.66   0.000     .0451895     .082908
      lncprt |  -.2271859   .0584759    -3.89   0.000    -.3417965   -.1125754
        mjpw |   1.905392   .2271861     8.39   0.000     1.460116    2.350669
     contigl |   -4.43133   .7304859    -6.07   0.000    -5.863057   -2.999603
      lndist |  -1.002773   .0982146   -10.21   0.000     -1.19527   -.8102763
    numstate |   .0076906   .0013044     5.90   0.000     .0051337    .0102475
    midyears |  -.4038567   .0274654   -14.70   0.000    -.4576878   -.3500256
   midyears2 |   .0156878   .0015225    10.30   0.000     .0127038    .0186718
   midyears3 |  -.0001775   .0000232    -7.64   0.000     -.000223    -.000132
       _cons |   1.940074   .7630578     2.54   0.011     .4445065    3.435641
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Imputations (20):
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Multiple-imputation estimates                     Imputations     =         20
Logistic regression                               Number of obs   =     423726
                                                  Average RVI     =     0.0358
                                                  Largest FMI     =     0.2193
DF adjustment:   Large sample                     DF:     min     =     409.28
                                                          avg     =   5.85e+07
                                                          max     =   6.14e+08
Model F test:       Equal FMI                     F(  11,203555.0)=     186.95
Within VCE type:       Robust                     Prob > F        =     0.0000

                            (Within VCE adjusted for 13538 clusters in dyadid)
------------------------------------------------------------------------------
     midongl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        CIEl |  -.1533177   .0547355    -2.80   0.005    -.2609155   -.0457199
         dml |  -.1018194   .0122899    -8.28   0.000    -.1259077   -.0777311
         dmh |   .0606118   .0092288     6.57   0.000     .0425236       .0787
      lncprt |  -.2538295   .0493233    -5.15   0.000    -.3505014   -.1571576
        mjpw |   2.086507   .2037596    10.24   0.000     1.687146    2.485869
     contigl |  -3.611159    .697285    -5.18   0.000    -4.977814   -2.244504
      lndist |  -.8590107   .0905561    -9.49   0.000    -1.036497   -.6815239
    numstate |   .0082845   .0011743     7.05   0.000     .0059823    .0105867
    midyears |  -.5777906   .0272864   -21.18   0.000     -.631271   -.5243101
   midyears2 |   .0244032   .0015356    15.89   0.000     .0213934     .027413
   midyears3 |  -.0002937   .0000239   -12.27   0.000    -.0003406   -.0002468
       _cons |   1.552976   .7223702     2.15   0.032     .1371546    2.968798
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Imputations (20):
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Multiple-imputation estimates                     Imputations     =         20
Logistic regression                               Number of obs   =     422735
                                                  Average RVI     =     0.0461
                                                  Largest FMI     =     0.1754
DF adjustment:   Large sample                     DF:     min     =     636.00
                                                          avg     =   4.01e+07
                                                          max     =   2.63e+08
Model F test:       Equal FMI                     F(  11,133765.0)=      54.58
Within VCE type:       Robust                     Prob > F        =     0.0000

                            (Within VCE adjusted for 13538 clusters in dyadid)
------------------------------------------------------------------------------
    fmidonsl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        CIEl |  -.4729832   .1497993    -3.16   0.002    -.7671443   -.1788222
         dml |  -.0749115   .0360341    -2.08   0.038    -.1455379    -.004285
         dmh |    .062701   .0262114     2.39   0.017     .0113276    .1140745
      lncprt |  -.3416606   .1113055    -3.07   0.002    -.5598155   -.1235056
        mjpw |    1.54847   .4809175     3.22   0.001     .6058885    2.491052
     contigl |   2.941779   2.829319     1.04   0.298    -2.603584    8.487143
      lndist |  -.2269684   .3570013    -0.64   0.525    -.9266781    .4727414
    numstate |   .0129303   .0034284     3.77   0.000      .006209    .0196517
   fmidyears |  -.4484287   .0748556    -5.99   0.000    -.5951431   -.3017144
  fmidyears2 |    .015183   .0043075     3.52   0.000     .0067404    .0236256
  fmidyears3 |  -.0001452   .0000656    -2.21   0.027    -.0002738   -.0000167
       _cons |  -7.002133   2.819167    -2.48   0.013     -12.5276   -1.476668
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Imputations (20):
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Multiple-imputation estimates                     Imputations     =         20
Logistic regression                               Number of obs   =     423430
                                                  Average RVI     =     0.1021
                                                  Largest FMI     =     0.3589
DF adjustment:   Large sample                     DF:     min     =     154.55
                                                          avg     = 4379482.20
                                                          max     =   1.72e+07
Model F test:       Equal FMI                     F(  11,27246.3) =      63.00
Within VCE type:       Robust                     Prob > F        =     0.0000

                            (Within VCE adjusted for 13538 clusters in dyadid)
------------------------------------------------------------------------------
    fmidongl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        CIEl |  -.3818799   .0897315    -4.26   0.000    -.5591385   -.2046213
         dml |  -.1207991   .0252673    -4.78   0.000    -.1703228   -.0712755
         dmh |   .0674823   .0154291     4.37   0.000     .0372417    .0977228
      lncprt |  -.3487596   .0737682    -4.73   0.000    -.4933427   -.2041765
        mjpw |   2.119127    .329285     6.44   0.000      1.47374    2.764514
     contigl |  -1.266511    1.36731    -0.93   0.354    -3.946391    1.413368
      lndist |  -.5917055   .1698563    -3.48   0.000    -.9246178   -.2587931
    numstate |   .0068222   .0019469     3.50   0.000     .0030035    .0106408
   fmidyears |  -.6559633   .0435173   -15.07   0.000    -.7412556    -.570671
  fmidyears2 |   .0266932   .0022485    11.87   0.000     .0222861    .0311003
  fmidyears3 |  -.0002988   .0000323    -9.25   0.000    -.0003621   -.0002355
       _cons |  -.9897025   1.413365    -0.70   0.484     -3.75985    1.780445
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Imputations (20):
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Multiple-imputation estimates                     Imputations     =         20
Logistic regression                               Number of obs   =     423001
                                                  Average RVI     =     0.0260
                                                  Largest FMI     =     0.0890
DF adjustment:   Large sample                     DF:     min     =    2438.44
                                                          avg     =   2.93e+07
                                                          max     =   1.28e+08
Model F test:       Equal FMI                     F(  12,325287.7)=     158.23
Within VCE type:       Robust                     Prob > F        =     0.0000

                            (Within VCE adjusted for 13538 clusters in dyadid)
------------------------------------------------------------------------------
     midonsl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       CIElc |  -.2295095   .0625893    -3.67   0.000    -.3522432   -.1067758
         dml |  -.0783534   .0109787    -7.14   0.000    -.0998719   -.0568348
    dmlCIElc |  -.0245567   .0065767    -3.73   0.000    -.0374503    -.011663
         dmh |   .0593444   .0095741     6.20   0.000     .0405794    .0781094
      lncprt |  -.2398747   .0581816    -4.12   0.000    -.3539084   -.1258409
        mjpw |   2.005562   .2230542     8.99   0.000     1.568384     2.44274
     contigl |  -4.453346   .7325496    -6.08   0.000    -5.889118   -3.017574
      lndist |  -1.010629   .0982074   -10.29   0.000    -1.203113   -.8181463
    numstate |   .0071485   .0013071     5.47   0.000     .0045863    .0097106
    midyears |  -.4001332    .027324   -14.64   0.000    -.4536874   -.3465791
   midyears2 |   .0154322    .001515    10.19   0.000     .0124629    .0184016
   midyears3 |  -.0001732   .0000231    -7.49   0.000    -.0002185   -.0001278
       _cons |    1.85763   .7528417     2.47   0.014     .3820874    3.333173
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Imputations (20):
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Multiple-imputation estimates                     Imputations     =         20
Logistic regression                               Number of obs   =     423726
                                                  Average RVI     =     0.0446
                                                  Largest FMI     =     0.1185
DF adjustment:   Large sample                     DF:     min     =    1381.86
                                                          avg     =   2.70e+07
                                                          max     =   1.05e+08
Model F test:       Equal FMI                     F(  12,116194.2)=     168.54
Within VCE type:       Robust                     Prob > F        =     0.0000

                            (Within VCE adjusted for 13538 clusters in dyadid)
------------------------------------------------------------------------------
     midongl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       CIElc |  -.2356714   .0600016    -3.93   0.000    -.3533756   -.1179672
         dml |  -.0995239   .0110479    -9.01   0.000    -.1211777     -.07787
    dmlCIElc |  -.0210698   .0062491    -3.37   0.001    -.0333274   -.0088122
         dmh |   .0567103   .0092036     6.16   0.000     .0386715    .0747491
      lncprt |  -.2623834   .0489463    -5.36   0.000    -.3583163   -.1664505
        mjpw |   2.161726   .1998127    10.82   0.000       1.7701    2.553351
     contigl |  -3.628462   .7028406    -5.16   0.000    -5.006006   -2.250918
      lndist |  -.8645745   .0911118    -9.49   0.000     -1.04315   -.6859986
    numstate |    .007914   .0011758     6.73   0.000     .0056087    .0102193
    midyears |  -.5747458   .0273225   -21.04   0.000     -.628297   -.5211947
   midyears2 |   .0241867   .0015369    15.74   0.000     .0211744    .0271991
   midyears3 |  -.0002901    .000024   -12.10   0.000    -.0003371   -.0002431
       _cons |    1.39039   .7201028     1.93   0.054    -.0209862    2.801766
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Imputations (20):
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Multiple-imputation estimates                     Imputations     =         20
Logistic regression                               Number of obs   =     422735
                                                  Average RVI     =     0.0593
                                                  Largest FMI     =     0.1818
DF adjustment:   Large sample                     DF:     min     =     592.74
                                                          avg     =   2.95e+07
                                                          max     =   1.94e+08
Model F test:       Equal FMI                     F(  12,74775.7) =      48.68
Within VCE type:       Robust                     Prob > F        =     0.0000

                            (Within VCE adjusted for 13538 clusters in dyadid)
------------------------------------------------------------------------------
    fmidonsl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       CIElc |  -.7159688   .1456216    -4.92   0.000    -1.001966   -.4299718
         dml |  -.1207493   .0302724    -3.99   0.000    -.1800918   -.0614067
    dmlCIElc |  -.0554223   .0140278    -3.95   0.000     -.082969   -.0278756
         dmh |   .0612793   .0260538     2.35   0.019     .0102149    .1123437
      lncprt |  -.3602617   .1127626    -3.19   0.001    -.5812726   -.1392509
        mjpw |   1.748628   .4720665     3.70   0.000     .8233935    2.673862
     contigl |   2.921242   2.924643     1.00   0.318    -2.810953    8.653436
      lndist |  -.2362042   .3684566    -0.64   0.521    -.9583659    .4859576
    numstate |   .0132103   .0034287     3.85   0.000     .0064881    .0199325
   fmidyears |  -.4464634   .0746562    -5.98   0.000    -.5927868   -.3001399
  fmidyears2 |   .0148798   .0043204     3.44   0.001     .0064119    .0233477
  fmidyears3 |  -.0001397    .000066    -2.12   0.034    -.0002691   -.0000103
       _cons |  -8.152627   2.910734    -2.80   0.005    -13.85757   -2.447686
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Imputations (20):
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Multiple-imputation estimates                     Imputations     =         20
Logistic regression                               Number of obs   =     423430
                                                  Average RVI     =     0.1147
                                                  Largest FMI     =     0.2690
DF adjustment:   Large sample                     DF:     min     =     273.34
                                                          avg     = 5556606.11
                                                          max     =   3.44e+07
Model F test:       Equal FMI                     F(  12,21792.7) =      59.47
Within VCE type:       Robust                     Prob > F        =     0.0000

                            (Within VCE adjusted for 13538 clusters in dyadid)
------------------------------------------------------------------------------
    fmidongl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       CIElc |   -.583964   .0921312    -6.34   0.000    -.7653409    -.402587
         dml |  -.1483565   .0232048    -6.39   0.000    -.1938404   -.1028726
    dmlCIElc |  -.0394531   .0099796    -3.95   0.000    -.0590664   -.0198397
         dmh |    .064214    .015312     4.19   0.000      .034203    .0942251
      lncprt |  -.3558027    .074007    -4.81   0.000    -.5008538   -.2107516
        mjpw |   2.219679   .3196042     6.95   0.000     1.593266    2.846092
     contigl |  -1.329193   1.397055    -0.95   0.341    -4.067373    1.408986
      lndist |  -.6023488    .173469    -3.47   0.001     -.942342   -.2623557
    numstate |   .0069803   .0019648     3.55   0.000      .003126    .0108346
   fmidyears |   -.652359   .0437949   -14.90   0.000    -.7381954   -.5665227
  fmidyears2 |   .0263691   .0022741    11.60   0.000     .0219119    .0308262
  fmidyears3 |  -.0002934   .0000327    -8.96   0.000    -.0003575   -.0002292
       _cons |  -1.858989     1.4394    -1.29   0.197    -4.680167    .9621891
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Imputations (20):
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Multiple-imputation estimates                     Imputations     =         20
Logistic regression                               Number of obs   =     423001
                                                  Average RVI     =     0.0190
                                                  Largest FMI     =     0.0795
DF adjustment:   Large sample                     DF:     min     =    3052.91
                                                          avg     =   5.39e+07
                                                          max     =   3.46e+08
Model F test:       Equal FMI                     F(  10, 1.1e+06)=     189.85
Within VCE type:       Robust                     Prob > F        =     0.0000

                            (Within VCE adjusted for 13538 clusters in dyadid)
------------------------------------------------------------------------------
     midonsl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
 lnlifepenlc |  -.2034058   .0521781    -3.90   0.000    -.3057136   -.1010979
         dml |  -.0475887   .0119965    -3.97   0.000    -.0711016   -.0240758
      lncprt |  -.2259636   .0614757    -3.68   0.000    -.3464537   -.1054734
        mjpw |   2.005175   .2484131     8.07   0.000     1.518295    2.492056
     contigl |  -4.260642   .7352206    -5.80   0.000    -5.701648   -2.819636
      lndist |  -.9486098   .1002013    -9.47   0.000    -1.145001   -.7522189
    numstate |   .0073478   .0013525     5.43   0.000     .0046969    .0099987
    midyears |  -.4188271   .0282331   -14.83   0.000     -.474163   -.3634913
   midyears2 |   .0163651   .0015505    10.55   0.000     .0133261    .0194041
   midyears3 |  -.0001849   .0000234    -7.90   0.000    -.0002308    -.000139
       _cons |   1.700677   .7753709     2.19   0.028     .1809781    3.220376
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Imputations (20):
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Multiple-imputation estimates                     Imputations     =         20
Logistic regression                               Number of obs   =     423726
                                                  Average RVI     =     0.0228
                                                  Largest FMI     =     0.1265
DF adjustment:   Large sample                     DF:     min     =    1214.37
                                                          avg     =   3.78e+07
                                                          max     =   1.55e+08
Model F test:       Equal FMI                     F(  10,582742.4)=     192.24
Within VCE type:       Robust                     Prob > F        =     0.0000

                            (Within VCE adjusted for 13538 clusters in dyadid)
------------------------------------------------------------------------------
     midongl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
 lnlifepenlc |  -.1741694   .0507853    -3.43   0.001    -.2738062   -.0745327
         dml |  -.0707357   .0122305    -5.78   0.000    -.0947073   -.0467641
      lncprt |  -.2561623    .051886    -4.94   0.000     -.357857   -.1544675
        mjpw |   2.207941   .2199185    10.04   0.000     1.776908    2.638973
     contigl |  -3.386155   .7082264    -4.78   0.000    -4.774253   -1.998057
      lndist |  -.8001902   .0936499    -8.54   0.000    -.9837406   -.6166397
    numstate |   .0078517   .0011891     6.60   0.000      .005521    .0101824
    midyears |  -.5933428   .0279776   -21.21   0.000    -.6481778   -.5385078
   midyears2 |   .0251081   .0015618    16.08   0.000     .0220471    .0281691
   midyears3 |  -.0003015   .0000241   -12.49   0.000    -.0003488   -.0002542
       _cons |   1.267958   .7325112     1.73   0.083    -.1677381    2.703653
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Imputations (20):
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Multiple-imputation estimates                     Imputations     =         20
Logistic regression                               Number of obs   =     422735
                                                  Average RVI     =     0.0160
                                                  Largest FMI     =     0.1070
DF adjustment:   Large sample                     DF:     min     =    1692.74
                                                          avg     =   1.78e+09
                                                          max     =   1.46e+10
Model F test:       Equal FMI                     F(  10,747142.7)=      95.01
Within VCE type:       Robust                     Prob > F        =     0.0000

                            (Within VCE adjusted for 13538 clusters in dyadid)
------------------------------------------------------------------------------
    fmidonsl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
 lnlifepenlc |  -.2638808   .1022025    -2.58   0.010    -.4643374   -.0634242
         dml |  -.0574387   .0309657    -1.85   0.064    -.1181306    .0032533
      lncprt |  -.3628717   .1103181    -3.29   0.001    -.5790912   -.1466523
        mjpw |    1.62512   .4812183     3.38   0.001     .6819494    2.568291
     contigl |   3.502043   2.784541     1.26   0.209    -1.955558    8.959644
      lndist |  -.1343835   .3552486    -0.38   0.705     -.830658    .5618909
    numstate |   .0092965   .0028626     3.25   0.001      .003686    .0149071
   fmidyears |   -.465274   .0744689    -6.25   0.000    -.6112303   -.3193176
  fmidyears2 |   .0159355   .0042494     3.75   0.000     .0076068    .0242642
  fmidyears3 |  -.0001528   .0000643    -2.38   0.017    -.0002787   -.0000268
       _cons |  -7.582748   2.784478    -2.72   0.006    -13.04023    -2.12527
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Imputations (20):
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Multiple-imputation estimates                     Imputations     =         20
Logistic regression                               Number of obs   =     423430
                                                  Average RVI     =     0.0520
                                                  Largest FMI     =     0.2992
DF adjustment:   Large sample                     DF:     min     =     221.41
                                                          avg     =   1.38e+08
                                                          max     =   7.73e+08
Model F test:       Equal FMI                     F(  10,72710.4) =      79.22
Within VCE type:       Robust                     Prob > F        =     0.0000

                            (Within VCE adjusted for 13538 clusters in dyadid)
------------------------------------------------------------------------------
    fmidongl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
 lnlifepenlc |  -.2316012   .0632721    -3.66   0.000    -.3562938   -.1069087
         dml |  -.0943756   .0236238    -3.99   0.000     -.140678   -.0480733
      lncprt |  -.3576355   .0750706    -4.76   0.000    -.5047711   -.2104998
        mjpw |   2.220052   .3610955     6.15   0.000     1.512318    2.927786
     contigl |  -.7471639   1.377582    -0.54   0.588    -3.447174    1.952846
      lndist |  -.4974317   .1768816    -2.81   0.005    -.8441132   -.1507501
    numstate |   .0042829   .0017326     2.47   0.013     .0008871    .0076788
   fmidyears |  -.6695859   .0445656   -15.02   0.000     -.756933   -.5822389
  fmidyears2 |   .0272677   .0022699    12.01   0.000     .0228187    .0317167
  fmidyears3 |  -.0003041   .0000322    -9.45   0.000    -.0003671    -.000241
       _cons |  -1.563666    1.42351    -1.10   0.272    -4.353696    1.226363
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Imputations (20):
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Multiple-imputation estimates                     Imputations     =         20
Logistic regression                               Number of obs   =     423001
                                                  Average RVI     =     0.0248
                                                  Largest FMI     =     0.0864
DF adjustment:   Large sample                     DF:     min     =    2584.37
                                                          avg     =   1.51e+07
                                                          max     =   4.75e+07
Model F test:       Equal FMI                     F(  11,476679.3)=     165.97
Within VCE type:       Robust                     Prob > F        =     0.0000

                              (Within VCE adjusted for 13538 clusters in dyadid)
--------------------------------------------------------------------------------
       midonsl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
   lnlifepenlc |  -.3706462   .0601645    -6.16   0.000    -.4885857   -.2527067
           dml |  -.0545956   .0109918    -4.97   0.000      -.07614   -.0330513
dmllnlifepenlc |  -.0309756   .0060685    -5.10   0.000    -.0428751   -.0190761
        lncprt |   -.236281   .0613929    -3.85   0.000    -.3566089    -.115953
          mjpw |   2.091602   .2464737     8.49   0.000     1.608522    2.574681
       contigl |  -4.395104   .7344401    -5.98   0.000     -5.83458   -2.955628
        lndist |  -.9694394   .0999094    -9.70   0.000    -1.165258   -.7736205
      numstate |   .0077254   .0013827     5.59   0.000     .0050154    .0104354
      midyears |   -.412758   .0281943   -14.64   0.000    -.4680179   -.3574981
     midyears2 |   .0159391    .001549    10.29   0.000     .0129031    .0189751
     midyears3 |  -.0001779   .0000234    -7.60   0.000    -.0002238    -.000132
         _cons |   1.790854    .779383     2.30   0.022      .263291    3.318417
--------------------------------------------------------------------------------
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Imputations (20):
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Multiple-imputation estimates                     Imputations     =         20
Logistic regression                               Number of obs   =     423726
                                                  Average RVI     =     0.0318
                                                  Largest FMI     =     0.1203
DF adjustment:   Large sample                     DF:     min     =    1342.46
                                                          avg     =   1.59e+07
                                                          max     =   5.14e+07
Model F test:       Equal FMI                     F(  11,245256.6)=     170.15
Within VCE type:       Robust                     Prob > F        =     0.0000

                              (Within VCE adjusted for 13538 clusters in dyadid)
--------------------------------------------------------------------------------
       midongl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
   lnlifepenlc |  -.3517706   .0570486    -6.17   0.000    -.4636067   -.2399344
           dml |  -.0789658    .011227    -7.03   0.000    -.1009714   -.0569602
dmllnlifepenlc |  -.0315152   .0056873    -5.54   0.000    -.0426722   -.0203583
        lncprt |  -.2639302    .051637    -5.11   0.000    -.3651369   -.1627236
          mjpw |   2.279338   .2176704    10.47   0.000     1.852712    2.705964
       contigl |  -3.492538   .7116039    -4.91   0.000    -4.887256    -2.09782
        lndist |   -.817113   .0939525    -8.70   0.000    -1.001256   -.6329696
      numstate |   .0081906   .0012143     6.74   0.000     .0058105    .0105707
      midyears |    -.58755   .0279693   -21.01   0.000    -.6423689   -.5327312
     midyears2 |   .0246975   .0015624    15.81   0.000     .0216354    .0277597
     midyears3 |  -.0002947   .0000242   -12.19   0.000     -.000342   -.0002473
         _cons |   1.322091   .7391294     1.79   0.074    -.1265766    2.770758
--------------------------------------------------------------------------------
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Imputations (20):
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Multiple-imputation estimates                     Imputations     =         20
Logistic regression                               Number of obs   =     422735
                                                  Average RVI     =     0.0269
                                                  Largest FMI     =     0.0882
DF adjustment:   Large sample                     DF:     min     =    2483.43
                                                          avg     =   7.68e+08
                                                          max     =   6.38e+09
Model F test:       Equal FMI                     F(  11,286905.4)=      81.47
Within VCE type:       Robust                     Prob > F        =     0.0000

                              (Within VCE adjusted for 13538 clusters in dyadid)
--------------------------------------------------------------------------------
      fmidonsl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
   lnlifepenlc |  -.4893511   .1096643    -4.46   0.000     -.704394   -.2743083
           dml |  -.0778941   .0329852    -2.36   0.018    -.1425441    -.013244
dmllnlifepenlc |  -.0394753    .010785    -3.66   0.000    -.0606199   -.0183307
        lncprt |  -.3762734   .1114674    -3.38   0.001    -.5947455   -.1578013
          mjpw |   1.742626   .4845775     3.60   0.000      .792871     2.69238
       contigl |   3.422411   2.843477     1.20   0.229    -2.150701    8.995523
        lndist |  -.1473253   .3621802    -0.41   0.684    -.8571854    .5625348
      numstate |     .01038   .0030218     3.44   0.001     .0044574    .0163026
     fmidyears |  -.4600354   .0747397    -6.16   0.000    -.6065225   -.3135483
    fmidyears2 |   .0155249   .0042596     3.64   0.000     .0071763    .0238735
    fmidyears3 |  -.0001464   .0000644    -2.28   0.023    -.0002726   -.0000203
         _cons |  -7.724803   2.846838    -2.71   0.007     -13.3045   -2.145102
--------------------------------------------------------------------------------
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Imputations (20):
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Multiple-imputation estimates                     Imputations     =         20
Logistic regression                               Number of obs   =     423430
                                                  Average RVI     =     0.0739
                                                  Largest FMI     =     0.1918
DF adjustment:   Large sample                     DF:     min     =     532.97
                                                          avg     =   8.06e+07
                                                          max     =   4.59e+08
Model F test:       Equal FMI                     F(  11,39636.5) =      86.63
Within VCE type:       Robust                     Prob > F        =     0.0000

                              (Within VCE adjusted for 13538 clusters in dyadid)
--------------------------------------------------------------------------------
      fmidongl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
   lnlifepenlc |  -.4708078   .0675914    -6.97   0.000    -.6035035   -.3381121
           dml |  -.1180006   .0257934    -4.57   0.000     -.168555   -.0674461
dmllnlifepenlc |  -.0414665   .0068548    -6.05   0.000    -.0549322   -.0280008
        lncprt |  -.3662289    .075575    -4.85   0.000    -.5143532   -.2181046
          mjpw |    2.30485   .3572369     6.45   0.000     1.604678    3.005021
       contigl |    -.85783     1.3965    -0.61   0.539    -3.594919    1.879259
        lndist |  -.5140701   .1790297    -2.87   0.004    -.8649618   -.1631785
      numstate |   .0050256   .0017857     2.81   0.005     .0015256    .0085256
     fmidyears |  -.6642411   .0446619   -14.87   0.000    -.7517768   -.5767055
    fmidyears2 |   .0268552   .0022804    11.78   0.000     .0223856    .0313247
    fmidyears3 |  -.0002973   .0000324    -9.19   0.000    -.0003607   -.0002339
         _cons |  -1.652163   1.444188    -1.14   0.253    -4.482721    1.178394
--------------------------------------------------------------------------------
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Imputations (20):
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Multiple-imputation estimates                     Imputations     =         20
Logistic regression                               Number of obs   =     423001
                                                  Average RVI     =     0.0201
                                                  Largest FMI     =     0.0970
DF adjustment:   Large sample                     DF:     min     =    2056.56
                                                          avg     =   2.65e+07
                                                          max     =   1.59e+08
Model F test:       Equal FMI                     F(  11,995740.2)=     182.68
Within VCE type:       Robust                     Prob > F        =     0.0000

                            (Within VCE adjusted for 13538 clusters in dyadid)
------------------------------------------------------------------------------
     midonsl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
 lnlifepenlc |  -.2174431    .045189    -4.81   0.000     -.306064   -.1288221
         dml |  -.0811703   .0116452    -6.97   0.000    -.1039948   -.0583458
         dmh |   .0621823   .0092312     6.74   0.000     .0440896    .0802751
      lncprt |  -.2197921    .057722    -3.81   0.000     -.332925   -.1066592
        mjpw |   1.903287   .2236418     8.51   0.000     1.464957    2.341617
     contigl |  -4.268634   .7254988    -5.88   0.000    -5.690586   -2.846683
      lndist |  -.9783053   .0972256   -10.06   0.000    -1.168864   -.7877467
    numstate |   .0075092    .001312     5.72   0.000     .0049377    .0100808
    midyears |  -.4038345   .0275811   -14.64   0.000    -.4578925   -.3497766
   midyears2 |   .0156548   .0015238    10.27   0.000     .0126682    .0186415
   midyears3 |  -.0001768   .0000231    -7.64   0.000    -.0002221   -.0001314
       _cons |   1.413808   .7623622     1.85   0.064    -.0803949     2.90801
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Imputations (20):
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Multiple-imputation estimates                     Imputations     =         20
Logistic regression                               Number of obs   =     423726
                                                  Average RVI     =     0.0271
                                                  Largest FMI     =     0.1677
DF adjustment:   Large sample                     DF:     min     =     695.15
                                                          avg     =   1.58e+07
                                                          max     =   5.32e+07
Model F test:       Equal FMI                     F(  11,420444.6)=     189.16
Within VCE type:       Robust                     Prob > F        =     0.0000

                            (Within VCE adjusted for 13538 clusters in dyadid)
------------------------------------------------------------------------------
     midongl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
 lnlifepenlc |  -.1931056   .0443489    -4.35   0.000    -.2801796   -.1060317
         dml |  -.1013797   .0117167    -8.65   0.000    -.1243443   -.0784151
         dmh |   .0585291   .0089689     6.53   0.000     .0409505    .0761077
      lncprt |   -.247008    .048622    -5.08   0.000    -.3423053   -.1517107
        mjpw |   2.074199   .1992338    10.41   0.000     1.683707     2.46469
     contigl |  -3.428713   .6938843    -4.94   0.000    -4.788701   -2.068725
      lndist |  -.8326514    .089911    -9.26   0.000    -1.008874   -.6564291
    numstate |    .007867   .0011539     6.82   0.000     .0056054    .0101287
    midyears |  -.5786891   .0274223   -21.10   0.000    -.6324358   -.5249425
   midyears2 |   .0244216   .0015374    15.88   0.000     .0214083     .027435
   midyears3 |  -.0002937   .0000239   -12.31   0.000    -.0003404   -.0002469
       _cons |   1.059343   .7199879     1.47   0.141    -.3518075    2.470493
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Imputations (20):
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Multiple-imputation estimates                     Imputations     =         20
Logistic regression                               Number of obs   =     422735
                                                  Average RVI     =     0.0234
                                                  Largest FMI     =     0.1459
DF adjustment:   Large sample                     DF:     min     =     915.91
                                                          avg     =   2.34e+09
                                                          max     =   2.30e+10
Model F test:       Equal FMI                     F(  11,370840.8)=      79.67
Within VCE type:       Robust                     Prob > F        =     0.0000

                            (Within VCE adjusted for 13538 clusters in dyadid)
------------------------------------------------------------------------------
    fmidonsl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
 lnlifepenlc |  -.2457083   .0861245    -2.85   0.004    -.4147325    -.076684
         dml |  -.0961455   .0313822    -3.06   0.002    -.1576539   -.0346372
         dmh |    .057008   .0240239     2.37   0.018     .0099219     .104094
      lncprt |  -.3543569   .1078475    -3.29   0.001    -.5657341   -.1429796
        mjpw |   1.605474   .4688635     3.42   0.001     .6865186     2.52443
     contigl |   3.437422   2.756426     1.25   0.212    -1.965072    8.839917
      lndist |  -.1650661   .3491646    -0.47   0.636     -.849416    .5192839
    numstate |   .0096485   .0027682     3.49   0.000     .0042228    .0150742
   fmidyears |  -.4552464   .0743782    -6.12   0.000    -.6010249   -.3094678
  fmidyears2 |   .0155068   .0042781     3.62   0.000     .0071218    .0238918
  fmidyears3 |  -.0001491   .0000651    -2.29   0.022    -.0002767   -.0000214
       _cons |  -7.847813   2.776329    -2.83   0.005    -13.28932   -2.406309
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Imputations (20):
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Multiple-imputation estimates                     Imputations     =         20
Logistic regression                               Number of obs   =     423430
                                                  Average RVI     =     0.0775
                                                  Largest FMI     =     0.3964
DF adjustment:   Large sample                     DF:     min     =     126.91
                                                          avg     =   6.80e+07
                                                          max     =   4.12e+08
Model F test:       Equal FMI                     F(  11,36241.6) =      80.94
Within VCE type:       Robust                     Prob > F        =     0.0000

                            (Within VCE adjusted for 13538 clusters in dyadid)
------------------------------------------------------------------------------
    fmidongl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
 lnlifepenlc |  -.2379911    .054778    -4.34   0.000    -.3463877   -.1295945
         dml |  -.1296207   .0235676    -5.50   0.000    -.1758127   -.0834288
         dmh |   .0607767   .0144896     4.19   0.000     .0323777    .0891757
      lncprt |  -.3457879   .0713865    -4.84   0.000     -.485703   -.2058729
        mjpw |   2.117141   .3245175     6.52   0.000     1.481098    2.753184
     contigl |  -.7698756   1.372636    -0.56   0.575    -3.460193    1.920442
      lndist |  -.5274399   .1713572    -3.08   0.002    -.8632938    -.191586
    numstate |   .0045067   .0016561     2.72   0.007     .0012607    .0077527
   fmidyears |  -.6590988   .0434021   -15.19   0.000    -.7441653   -.5740322
  fmidyears2 |   .0268145   .0022339    12.00   0.000      .022436    .0311929
  fmidyears3 |  -.0003001    .000032    -9.38   0.000    -.0003628   -.0002374
       _cons |  -1.840312   1.439441    -1.28   0.201    -4.661565    .9809414
------------------------------------------------------------------------------
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)

Imputations (20):
  .........10.........20 done

Multiple-imputation estimates                     Imputations     =         20
Logistic regression                               Number of obs   =     423001
                                                  Average RVI     =     0.0256
                                                  Largest FMI     =     0.0854
DF adjustment:   Large sample                     DF:     min     =    2650.14
                                                          avg     =   1.01e+07
                                                          max     =   2.90e+07
Model F test:       Equal FMI                     F(  12,501087.5)=     163.58
Within VCE type:       Robust                     Prob > F        =     0.0000

                              (Within VCE adjusted for 13538 clusters in dyadid)
--------------------------------------------------------------------------------
       midonsl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
   lnlifepenlc |  -.3679439   .0582123    -6.32   0.000    -.4820561   -.2538316
           dml |  -.0861757   .0109781    -7.85   0.000    -.1076928   -.0646586
dmllnlifepenlc |  -.0273285   .0062379    -4.38   0.000    -.0395602   -.0150969
           dmh |   .0599731   .0092962     6.45   0.000     .0417528    .0781934
        lncprt |  -.2284734   .0577076    -3.96   0.000    -.3415782   -.1153685
          mjpw |   1.978997   .2232503     8.86   0.000     1.541435     2.41656
       contigl |  -4.379215   .7268767    -6.02   0.000    -5.803867   -2.954563
        lndist |  -.9948892   .0973023   -10.22   0.000    -1.185598   -.8041802
      numstate |   .0078409   .0013349     5.87   0.000     .0052245    .0104572
      midyears |     -.3986   .0275412   -14.47   0.000    -.4525798   -.3446202
     midyears2 |   .0152906   .0015231    10.04   0.000     .0123053    .0182758
     midyears3 |  -.0001708   .0000232    -7.37   0.000    -.0002162   -.0001254
         _cons |   1.490923    .768133     1.94   0.052    -.0145906    2.996436
--------------------------------------------------------------------------------
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)

Imputations (20):
  .........10.........20 done

Multiple-imputation estimates                     Imputations     =         20
Logistic regression                               Number of obs   =     423726
                                                  Average RVI     =     0.0354
                                                  Largest FMI     =     0.1161
DF adjustment:   Large sample                     DF:     min     =    1439.44
                                                          avg     = 8709664.05
                                                          max     =   2.43e+07
Model F test:       Equal FMI                     F(  12,221701.1)=     170.10
Within VCE type:       Robust                     Prob > F        =     0.0000

                              (Within VCE adjusted for 13538 clusters in dyadid)
--------------------------------------------------------------------------------
       midongl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
   lnlifepenlc |  -.3483531   .0550486    -6.33   0.000    -.4562711   -.2404351
           dml |  -.1074251   .0110978    -9.68   0.000     -.129177   -.0856733
dmllnlifepenlc |  -.0272398    .005946    -4.58   0.000    -.0389036   -.0155761
           dmh |   .0561252   .0090186     6.22   0.000     .0384492    .0738013
        lncprt |  -.2534879   .0485055    -5.23   0.000     -.348557   -.1584188
          mjpw |   2.137804   .1985928    10.76   0.000     1.748569    2.527039
       contigl |    -3.5132   .6984854    -5.03   0.000    -4.882206   -2.144194
        lndist |  -.8452836   .0904386    -9.35   0.000     -1.02254   -.6680273
      numstate |   .0081603   .0011724     6.96   0.000     .0058625    .0104582
      midyears |  -.5739175   .0274496   -20.91   0.000    -.6277179   -.5201172
     midyears2 |   .0240835   .0015401    15.64   0.000      .021065     .027102
     midyears3 |  -.0002881   .0000239   -12.04   0.000    -.0003349   -.0002412
         _cons |   1.104081   .7274297     1.52   0.129    -.3216549    2.529818
--------------------------------------------------------------------------------
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)

Imputations (20):
  .........10.........20 done

Multiple-imputation estimates                     Imputations     =         20
Logistic regression                               Number of obs   =     422735
                                                  Average RVI     =     0.0308
                                                  Largest FMI     =     0.0946
DF adjustment:   Large sample                     DF:     min     =    2159.87
                                                          avg     =   1.10e+09
                                                          max     =   1.05e+10
Model F test:       Equal FMI                     F(  12,238268.2)=      68.14
Within VCE type:       Robust                     Prob > F        =     0.0000

                              (Within VCE adjusted for 13538 clusters in dyadid)
--------------------------------------------------------------------------------
      fmidonsl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
   lnlifepenlc |  -.4779751   .1051781    -4.54   0.000    -.6842359   -.2717142
           dml |  -.1161014   .0332905    -3.49   0.000    -.1813498    -.050853
dmllnlifepenlc |  -.0397828   .0106212    -3.75   0.000    -.0606067   -.0189589
           dmh |   .0567318   .0244937     2.32   0.021      .008725    .1047387
        lncprt |  -.3678069    .108707    -3.38   0.001    -.5808686   -.1547452
          mjpw |    1.72039   .4711656     3.65   0.000     .7969224    2.643858
       contigl |   3.386339   2.818866     1.20   0.230    -2.138537    8.911215
        lndist |  -.1750218   .3564637    -0.49   0.623    -.8736779    .5236342
      numstate |   .0107085   .0029149     3.67   0.000     .0049953    .0164216
     fmidyears |    -.45041   .0746421    -6.03   0.000    -.5967057   -.3041142
    fmidyears2 |   .0151274   .0042946     3.52   0.000     .0067102    .0235447
    fmidyears3 |  -.0001432   .0000654    -2.19   0.028    -.0002714   -.0000151
         _cons |  -8.017155   2.841881    -2.82   0.005    -13.58714    -2.44717
--------------------------------------------------------------------------------
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)

Imputations (20):
  .........10.........20 done

Multiple-imputation estimates                     Imputations     =         20
Logistic regression                               Number of obs   =     423430
                                                  Average RVI     =     0.0870
                                                  Largest FMI     =     0.1786
DF adjustment:   Large sample                     DF:     min     =     614.20
                                                          avg     =   5.61e+07
                                                          max     =   3.69e+08
Model F test:       Equal FMI                     F(  12,32196.3) =      89.54
Within VCE type:       Robust                     Prob > F        =     0.0000

                              (Within VCE adjusted for 13538 clusters in dyadid)
--------------------------------------------------------------------------------
      fmidongl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
   lnlifepenlc |  -.4676809   .0643631    -7.27   0.000    -.5940793   -.3412824
           dml |  -.1510914    .025765    -5.86   0.000    -.2015901   -.1005928
dmllnlifepenlc |  -.0390723    .007273    -5.37   0.000    -.0533505   -.0247942
           dmh |   .0586208   .0145898     4.02   0.000     .0300253    .0872163
        lncprt |  -.3532813   .0718086    -4.92   0.000    -.4940235   -.2125391
          mjpw |   2.193207   .3215801     6.82   0.000     1.562922    2.823492
       contigl |  -.8606721   1.394123    -0.62   0.537    -3.593104     1.87176
        lndist |  -.5407906   .1739038    -3.11   0.002    -.8816358   -.1999455
      numstate |   .0051888   .0016895     3.07   0.002     .0018774    .0085003
     fmidyears |  -.6539697   .0436133   -14.99   0.000    -.7394502   -.5684892
    fmidyears2 |   .0264306   .0022515    11.74   0.000     .0220178    .0308434
    fmidyears3 |  -.0002939   .0000323    -9.11   0.000    -.0003571   -.0002306
         _cons |  -1.934008   1.460151    -1.32   0.185    -4.795853    .9278365
--------------------------------------------------------------------------------
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)

. 
. 
. 
. 
. 
. order specification specnumber DmlCoefficient DmlSE Dmlpvalues CIElCoefficient CIElSE CIElpvalues

. replace Dmlpvalues=2*normal(-abs(DmlCoefficient/DmlSE))
(32 real changes made)

. replace CIElpvalues=2*normal(-abs(CIElCoefficient/CIElSE))
(32 real changes made)

. keep specification specnumber DmlCoefficient DmlSE Dmlpvalues CIElCoefficient CIElSE CIElpvalues

. drop if specnumber==.
(436509 observations deleted)

. 
. 
. saveold "robustnessMdml.dta", replace
(note: file robustnessMdml.dta not found)
file robustnessMdml.dta saved

. 
. 
end of do-file

. *Produces: robustnessMdml.dta
. 
. do "12-09-24_loop_mi_robustness_bdm.do"

. *12-09-24_loop_mi_robustness_bdm.do
. 
. clear

. macro drop _all

. 
. global filetree /Users/Allan/Dropbox/!!Papers/Liberal Peace/12-02-21_ISQ_commentary/DOR_ISQ_2013_Replication/M_R
> ep

. 
. cd "$filetree"
/Users/Allan/Dropbox/!!Papers/Liberal Peace/12-02-21_ISQ_commentary/DOR_ISQ_2013_Replication/M_Rep

. use "midata"

. 
. mi xtset, clear

. 
. **check that logit runs with midyears*
. sort ccode1 ccode2 year

. global controls lncprt mjpw contigl lndist numstate

. 
. gen specification="NA"

. gen specnumber=.
(436541 missing values generated)

. gen n=.
(436541 missing values generated)

. gen bdmCoefficient=.
(436541 missing values generated)

. gen bdmSE=.
(436541 missing values generated)

. gen CIElCoefficient=.
(436541 missing values generated)

. gen CIElSE=.
(436541 missing values generated)

. gen bdmpvalues=.
(436541 missing values generated)

. gen CIElpvalues=.
(436541 missing values generated)

. 
. 
. gen bdm=.
(436541 missing values generated)

. replace bdm=1 if dml>6 & dml<11
(45417 real changes made)

. replace bdm=0 if dml<=6 & dml>=-10 
(391124 real changes made)

. 
. mi passive: generate bdmCIElc= bdm*CIElc
m=0:
(398484 missing values generated)
m=1:
m=2:
m=3:
m=4:
m=5:
m=6:
m=7:
m=8:
m=9:
m=10:
m=11:
m=12:
m=13:
m=14:
m=15:
m=16:
m=17:
m=18:
m=19:
m=20:

. 
. gen h10dm=.
(436541 missing values generated)

. replace h10dm=1 if dml>9 & dml<11
(13555 real changes made)

. replace h10dm=0 if dml<=9 & dml>=-10 
(422986 real changes made)

. 
. mi passive: generate h10dmCIElc= h10dm*CIElc
m=0:
(398484 missing values generated)
m=1:
m=2:
m=3:
m=4:
m=5:
m=6:
m=7:
m=8:
m=9:
m=10:
m=11:
m=12:
m=13:
m=14:
m=15:
m=16:
m=17:
m=18:
m=19:
m=20:

. 
. **Code just to see if things are running
. mi estimate, dots: logit fmidonsl lnlifedeerl bdm $controls fmidyears*, cl(dyadid) 

Imputations (20):
  .........10.........20 done

Multiple-imputation estimates                     Imputations     =         20
Logistic regression                               Number of obs   =     422735
                                                  Average RVI     =     0.0481
                                                  Largest FMI     =     0.1748
DF adjustment:   Large sample                     DF:     min     =     640.71
                                                          avg     =   1.25e+08
                                                          max     =   9.54e+08
Model F test:       Equal FMI                     F(  10,117124.7)=      71.96
Within VCE type:       Robust                     Prob > F        =     0.0000

                            (Within VCE adjusted for 13538 clusters in dyadid)
------------------------------------------------------------------------------
    fmidonsl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
 lnlifedeerl |  -.4802039   .1554805    -3.09   0.002    -.7855169   -.1748909
         bdm |  -.5418861   .6485877    -0.84   0.403    -1.813129    .7293571
      lncprt |  -.3602811   .1155692    -3.12   0.002    -.5867926   -.1337696
        mjpw |   1.582599   .4879373     3.24   0.001     .6262585    2.538939
     contigl |   2.992261      2.839     1.05   0.292    -2.572078    8.556599
      lndist |  -.2004175   .3604046    -0.56   0.578    -.9067976    .5059625
    numstate |   .0119439   .0036268     3.29   0.001     .0048337    .0190541
   fmidyears |  -.4561645   .0739953    -6.16   0.000    -.6011925   -.3111364
  fmidyears2 |   .0154876    .004258     3.64   0.000      .007142    .0238331
  fmidyears3 |  -.0001474    .000065    -2.27   0.023    -.0002749     -.00002
       _cons |   -6.36072    2.79287    -2.28   0.023    -11.83465   -.8867951
------------------------------------------------------------------------------

. logit midonsl _1_lnlifedeerl bdm $controls       midyears*, cl(dyadid) 

Iteration 0:   log pseudolikelihood = -9683.1099  
Iteration 1:   log pseudolikelihood = -7596.6499  
Iteration 2:   log pseudolikelihood = -6674.4405  
Iteration 3:   log pseudolikelihood = -6314.5745  
Iteration 4:   log pseudolikelihood = -6307.3455  
Iteration 5:   log pseudolikelihood = -6307.3276  
Iteration 6:   log pseudolikelihood = -6307.3276  

Logistic regression                               Number of obs   =     423001
                                                  Wald chi2(10)   =    1946.62
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -6307.3276                 Pseudo R2       =     0.3486

                               (Std. Err. adjusted for 13538 clusters in dyadid)
--------------------------------------------------------------------------------
               |               Robust
       midonsl |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
_1_lnlifedeerl |  -.1305888   .0595017    -2.19   0.028      -.24721   -.0139677
           bdm |   -.909569   .2789356    -3.26   0.001    -1.456273   -.3628653
        lncprt |  -.2372053   .0618971    -3.83   0.000    -.3585214   -.1158891
          mjpw |   1.997667   .2466668     8.10   0.000     1.514209    2.481125
       contigl |   -4.39161   .7296759    -6.02   0.000    -5.821749   -2.961472
        lndist |  -.9707153   .0996826    -9.74   0.000     -1.16609   -.7753411
      numstate |   .0064614   .0013359     4.84   0.000      .003843    .0090797
      midyears |   -.412434   .0281527   -14.65   0.000    -.4676123   -.3572558
     midyears2 |   .0161385   .0015619    10.33   0.000     .0130772    .0191998
     midyears3 |  -.0001829   .0000238    -7.68   0.000    -.0002297   -.0001362
         _cons |   2.641523   .7396028     3.57   0.000     1.191928    4.091118
--------------------------------------------------------------------------------

. logit midonsl _2_lnlifedeerl bdm $controls       midyears*, cl(dyadid) 

Iteration 0:   log pseudolikelihood = -9683.1099  
Iteration 1:   log pseudolikelihood =  -7600.017  
Iteration 2:   log pseudolikelihood = -6680.5092  
Iteration 3:   log pseudolikelihood = -6320.7566  
Iteration 4:   log pseudolikelihood = -6313.5488  
Iteration 5:   log pseudolikelihood =  -6313.531  
Iteration 6:   log pseudolikelihood =  -6313.531  

Logistic regression                               Number of obs   =     423001
                                                  Wald chi2(10)   =    1978.64
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood =  -6313.531                 Pseudo R2       =     0.3480

                               (Std. Err. adjusted for 13538 clusters in dyadid)
--------------------------------------------------------------------------------
               |               Robust
       midonsl |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
_2_lnlifedeerl |  -.0876169    .055262    -1.59   0.113    -.1959283    .0206946
           bdm |   -.993662   .2808639    -3.54   0.000    -1.544145   -.4431788
        lncprt |  -.2369895   .0616754    -3.84   0.000    -.3578709    -.116108
          mjpw |   1.986759   .2456692     8.09   0.000     1.505257    2.468262
       contigl |  -4.332595   .7300247    -5.93   0.000    -5.763417   -2.901773
        lndist |  -.9642831   .0997156    -9.67   0.000    -1.159722    -.768844
      numstate |   .0058792   .0012896     4.56   0.000     .0033516    .0084068
      midyears |  -.4135783   .0282259   -14.65   0.000       -.4689   -.3582566
     midyears2 |    .016211   .0015665    10.35   0.000     .0131408    .0192813
     midyears3 |   -.000184   .0000239    -7.70   0.000    -.0002309   -.0001371
         _cons |   2.611967    .739571     3.53   0.000     1.162434    4.061499
--------------------------------------------------------------------------------

. logit fmidonsl _3_lnlifedeerl bdm $controls      fmidyears*, cl(dyadid) nolog

Logistic regression                               Number of obs   =     422735
                                                  Wald chi2(10)   =     727.87
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -784.01436                 Pseudo R2       =     0.4121

                               (Std. Err. adjusted for 13538 clusters in dyadid)
--------------------------------------------------------------------------------
               |               Robust
      fmidonsl |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
_3_lnlifedeerl |  -.5499976   .1409513    -3.90   0.000     -.826257   -.2737382
           bdm |  -.4673609   .6009578    -0.78   0.437    -1.645216    .7104947
        lncprt |  -.3567639   .1163389    -3.07   0.002     -.584784   -.1287438
          mjpw |   1.553219   .4899641     3.17   0.002     .5929069    2.513531
       contigl |   2.983973   2.827546     1.06   0.291    -2.557916    8.525861
        lndist |  -.1987263   .3591922    -0.55   0.580      -.90273    .5052774
      numstate |   .0128704   .0036289     3.55   0.000      .005758    .0199828
     fmidyears |  -.4553897   .0736247    -6.19   0.000    -.5996916   -.3110879
    fmidyears2 |   .0154901   .0042277     3.66   0.000     .0072039    .0237763
    fmidyears3 |   -.000148   .0000645    -2.29   0.022    -.0002745   -.0000215
         _cons |   -6.40702   2.799384    -2.29   0.022    -11.89371   -.9203287
--------------------------------------------------------------------------------

. ****
. 
. sort ccode1 ccode2 year

. 
. local j=0

. 
. **M2 m.i. with 4 dependent variables x interaction = 8 models
. local j=`j'+1

. local spec`j' "M'"

. local covars`j' "midonsl CIEl  bdm                                         $controls midyears*"

. 
. 
. local j=`j'+1

. local spec`j' "OM'"

. local covars`j' "midongl CIEl  bdm                                         $controls midyears*"

. 
. 
. local j=`j'+1

. local spec`j' "FM'"

. local covars`j' "fmidonsl CIEl  bdm                                        $controls fmidyears*"

. 
. 
. local j=`j'+1

. local spec`j' "FOM'"

. local covars`j' "fmidongl CIEl  bdm                                        $controls fmidyears*"

. 
. local j=`j'+1

. local spec`j' "IM'"

. local covars`j' "midonsl CIElc  bdm bdmCIElc                                       $controls midyears*"

. 
. 
. local j=`j'+1

. local spec`j' "OIM'"

. local covars`j' "midongl CIElc  bdm      bdmCIElc                                  $controls midyears*"

. 
. 
. local j=`j'+1

. local spec`j' "FIM'"

. local covars`j' "fmidonsl CIElc  bdm             bdmCIElc                          $controls fmidyears*"

. 
. 
. local j=`j'+1

. local spec`j' "FOIM'"

. local covars`j' "fmidongl CIElc  bdm    bdmCIElc                                   $controls fmidyears*"

. 
. 
. 
. **M2 m.i. with DemocracyHigh with 4 dependent variables x interaction = 8 models
. 
. 
. **M2 with 4 dependent variables x interaction = 8 models
. local j=`j'+1

. local spec`j' "DM'"

. local covars`j' "midonsl CIEl  bdm  dmh                                            $controls midyears*"

. 
. 
. local j=`j'+1

. local spec`j' "ODM'"

. local covars`j' "midongl CIEl  bdm  dmh                                            $controls midyears*"

. 
. 
. local j=`j'+1

. local spec`j' "FDM'"

. local covars`j' "fmidonsl CIEl  bdm  dmh                                           $controls fmidyears*"

. 
. 
. local j=`j'+1

. local spec`j' "FODM'"

. local covars`j'  "fmidongl CIEl  bdm  dmh                                          $controls fmidyears*"

. 
. 
. local j=`j'+1

. local spec`j'  "IDM'"

. local covars`j'  "midonsl CIElc  bdm bdmCIElc    dmh                               $controls midyears*"

. 
. 
. local j=`j'+1

. local spec`j'  "OIDM'"

. local covars`j'  "midongl CIElc  bdm     bdmCIElc        dmh                       $controls midyears*"

. 
. 
. local j=`j'+1

. local spec`j'  "FIDM'"

. local covars`j'  "fmidonsl CIElc  bdm            bdmCIElc        dmh               $controls fmidyears*"

. 
. 
. local j=`j'+1

. local spec`j'  "FOIDM'"

. local covars`j' "fmidongl CIElc  bdm    bdmCIElc         dmh                       $controls fmidyears*"

. 
. 
. 
. 
. ****With other measures of life insurance expenditures
. 
. 
. **M2 m.i. with 4 dependent variables x interaction = 8 models
. local j=`j'+1

. local spec`j' "L'"

. local covars`j' "midonsl lnlifepenl  bdm                                           $controls midyears*"

. 
. 
. local j=`j'+1

. local spec`j' "OL'"

. local covars`j' "midongl lnlifepenl  bdm                                           $controls midyears*"

. 
. 
. local j=`j'+1

. local spec`j' "FL'"

. local covars`j' "fmidonsl lnlifepenl  bdm                                          $controls fmidyears*"

. 
. 
. local j=`j'+1

. local spec`j' "FOL'"

. local covars`j' "fmidongl lnlifepenl  bdm                                          $controls fmidyears*"

. 
. local j=`j'+1

. local spec`j' "IL'"

. local covars`j' "midonsl CIElc  bdm bdmCIElc                                       $controls midyears*"

. 
. 
. local j=`j'+1

. local spec`j' "OIL'"

. local covars`j' "midongl CIElc  bdm      bdmCIElc                                  $controls midyears*"

. 
. 
. local j=`j'+1

. local spec`j' "FIL'"

. local covars`j' "fmidonsl CIElc  bdm             bdmCIElc                          $controls fmidyears*"

. 
. 
. local j=`j'+1

. local spec`j' "FOIL'"

. local covars`j' "fmidongl CIElc  bdm    bdmCIElc                                   $controls fmidyears*"

. 
. 
. 
. **M2 m.i. with DemocracyHigh with 4 dependent variables x interaction = 8 models
. 
. local j=`j'+1

. local spec`j' "DL'"

. local covars`j' "midonsl lnlifepenl  bdm  dmh                                      $controls midyears*"

. 
. 
. local j=`j'+1

. local spec`j' "ODL'"

. local covars`j' "midongl lnlifepenl  bdm  dmh                                      $controls midyears*"

. 
. 
. local j=`j'+1

. local spec`j' "FDL'"

. local covars`j' "fmidonsl lnlifepenl  bdm  dmh                                     $controls fmidyears*"

. 
. 
. local j=`j'+1

. local spec`j' "FODL'"

. local covars`j'  "fmidongl lnlifepenl  bdm  dmh                                    $controls fmidyears*"

. 
. 
. local j=`j'+1

. local spec`j'  "IDL'"

. local covars`j'  "midonsl CIElc  bdm bdmCIElc    dmh                               $controls midyears*"

. 
. 
. local j=`j'+1

. local spec`j'  "OIDL'"

. local covars`j'  "midongl CIElc  bdm     bdmCIElc        dmh                       $controls midyears*"

. 
. 
. local j=`j'+1

. local spec`j'  "FIDL'"

. local covars`j'  "fmidonsl CIElc  bdm            bdmCIElc        dmh               $controls fmidyears*"

. 
. 
. local j=`j'+1

. local spec`j'  "FOIDL'"

. local covars`j' "fmidongl CIElc  bdm    bdmCIElc         dmh                       $controls fmidyears*"

. 
. 
. sort ccode1 ccode2 year

. 
. **Estimating Models, Saving Values
. forvalues k=1(1)`j' {
  2. replace specification="`spec`k''" if _n==`k'
  3. mi estimate, dots: logit `covars`k'', cl(dyadid) nolog
  4. replace n= e(N)   if _n==`k'
  5. mat A=e(b_mi)
  6. mat V=e(V_mi)
  7. replace specnumber=`k' if _n==`k'
  8. replace bdmCoefficient=A[1,2] if _n==`k'
  9. replace bdmSE=(V[2,2])^(1/2) if _n==`k'
 10. replace CIElCoefficient=A[1,1] if _n==`k'
 11. replace CIElSE=(V[1,1])^(1/2) if _n==`k'
 12. 
. }
(1 real change made)

Imputations (20):
  .........10.........20 done

Multiple-imputation estimates                     Imputations     =         20
Logistic regression                               Number of obs   =     423001
                                                  Average RVI     =     0.0234
                                                  Largest FMI     =     0.1510
DF adjustment:   Large sample                     DF:     min     =     855.98
                                                          avg     =   9.72e+08
                                                          max     =   1.04e+10
Model F test:       Equal FMI                     F(  10,375105.9)=     193.22
Within VCE type:       Robust                     Prob > F        =     0.0000

                            (Within VCE adjusted for 13538 clusters in dyadid)
------------------------------------------------------------------------------
     midonsl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        CIEl |  -.1097038   .0611451    -1.79   0.073    -.2297157    .0103082
         bdm |  -.9474331   .2827667    -3.35   0.001    -1.501674   -.3931923
      lncprt |  -.2373513   .0618475    -3.84   0.000    -.3585702   -.1161325
        mjpw |   1.995084   .2465863     8.09   0.000     1.511783    2.478384
     contigl |  -4.373893   .7327976    -5.97   0.000    -5.810151   -2.937635
      lndist |  -.9689347   .1000356    -9.69   0.000    -1.165001   -.7728685
    numstate |   .0062072   .0013467     4.61   0.000     .0035674     .008847
    midyears |  -.4130572   .0281847   -14.66   0.000    -.4682983   -.3578161
   midyears2 |   .0161853   .0015648    10.34   0.000     .0131183    .0192523
   midyears3 |  -.0001837   .0000239    -7.69   0.000    -.0002305   -.0001369
       _cons |   2.626757   .7414291     3.54   0.000     1.173582    4.079931
------------------------------------------------------------------------------
(1 real change made)
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(1 real change made)
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(1 real change made)
(1 real change made)
specification was str2 now str3
(1 real change made)

Imputations (20):
  .........10.........20 done

Multiple-imputation estimates                     Imputations     =         20
Logistic regression                               Number of obs   =     423726
                                                  Average RVI     =     0.0366
                                                  Largest FMI     =     0.2090
DF adjustment:   Large sample                     DF:     min     =     449.85
                                                          avg     =   2.29e+08
                                                          max     =   2.26e+09
Model F test:       Equal FMI                     F(  10,160681.0)=     193.32
Within VCE type:       Robust                     Prob > F        =     0.0000

                            (Within VCE adjusted for 13538 clusters in dyadid)
------------------------------------------------------------------------------
     midongl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        CIEl |  -.1297763   .0604229    -2.15   0.032    -.2485224   -.0110302
         bdm |  -1.119352   .2914098    -3.84   0.000    -1.690536   -.5481684
      lncprt |  -.2673765    .052582    -5.08   0.000    -.3704354   -.1643175
        mjpw |   2.203786   .2236626     9.85   0.000     1.765415    2.642156
     contigl |  -3.530725   .7070798    -4.99   0.000    -4.916577   -2.144872
      lndist |  -.8239264    .093593    -8.80   0.000    -1.007365   -.6404874
    numstate |   .0066911   .0012304     5.44   0.000     .0042786    .0091036
    midyears |  -.5845172   .0281148   -20.79   0.000    -.6396212   -.5294132
   midyears2 |   .0248575   .0015927    15.61   0.000     .0217359    .0279791
   midyears3 |  -.0003009    .000025   -12.05   0.000    -.0003498   -.0002519
       _cons |   2.354711   .7079293     3.33   0.001     .9671942    3.742227
------------------------------------------------------------------------------
(1 real change made)
(1 real change made)
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(1 real change made)
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(1 real change made)

Imputations (20):
  .........10.........20 done

Multiple-imputation estimates                     Imputations     =         20
Logistic regression                               Number of obs   =     422735
                                                  Average RVI     =     0.0481
                                                  Largest FMI     =     0.1748
DF adjustment:   Large sample                     DF:     min     =     640.71
                                                          avg     =   1.25e+08
                                                          max     =   9.54e+08
Model F test:       Equal FMI                     F(  10,117124.7)=      71.96
Within VCE type:       Robust                     Prob > F        =     0.0000

                            (Within VCE adjusted for 13538 clusters in dyadid)
------------------------------------------------------------------------------
    fmidonsl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        CIEl |  -.4802039   .1554805    -3.09   0.002    -.7855169   -.1748909
         bdm |  -.5418861   .6485877    -0.84   0.403    -1.813129    .7293571
      lncprt |  -.3602811   .1155692    -3.12   0.002    -.5867926   -.1337696
        mjpw |   1.582599   .4879373     3.24   0.001     .6262585    2.538939
     contigl |   2.992261      2.839     1.05   0.292    -2.572078    8.556599
      lndist |  -.2004175   .3604046    -0.56   0.578    -.9067976    .5059625
    numstate |   .0119439   .0036268     3.29   0.001     .0048337    .0190541
   fmidyears |  -.4561645   .0739953    -6.16   0.000    -.6011925   -.3111364
  fmidyears2 |   .0154876    .004258     3.64   0.000      .007142    .0238331
  fmidyears3 |  -.0001474    .000065    -2.27   0.023    -.0002749     -.00002
       _cons |   -6.36072    2.79287    -2.28   0.023    -11.83465   -.8867951
------------------------------------------------------------------------------
(1 real change made)
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specification was str3 now str4
(1 real change made)

Imputations (20):
  .........10.........20 done

Multiple-imputation estimates                     Imputations     =         20
Logistic regression                               Number of obs   =     423430
                                                  Average RVI     =     0.1017
                                                  Largest FMI     =     0.3117
DF adjustment:   Large sample                     DF:     min     =     204.31
                                                          avg     = 6102454.27
                                                          max     =   1.67e+07
Model F test:       Equal FMI                     F(  10,26809.3) =      64.28
Within VCE type:       Robust                     Prob > F        =     0.0000

                            (Within VCE adjusted for 13538 clusters in dyadid)
------------------------------------------------------------------------------
    fmidongl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        CIEl |  -.3851362   .0994999    -3.87   0.000    -.5813144    -.188958
         bdm |  -.9841937   .5538484    -1.78   0.076     -2.06975    .1013621
      lncprt |  -.3657885   .0780231    -4.69   0.000     -.518711    -.212866
        mjpw |    2.22942    .363305     6.14   0.000     1.517355    2.941485
     contigl |  -1.229396   1.359757    -0.90   0.366    -3.894472     1.43568
      lndist |  -.5619159    .173557    -3.24   0.001    -.9020814   -.2217505
    numstate |    .005356   .0021534     2.49   0.013     .0011321    .0095798
   fmidyears |  -.6560861   .0444914   -14.75   0.000    -.7432876   -.5688845
  fmidyears2 |   .0267663   .0022873    11.70   0.000     .0222834    .0312493
  fmidyears3 |  -.0003002   .0000329    -9.14   0.000    -.0003646   -.0002359
       _cons |  -.0197314   1.359226    -0.01   0.988    -2.683767    2.644304
------------------------------------------------------------------------------
(1 real change made)
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Imputations (20):
  .........10.........20 done

Multiple-imputation estimates                     Imputations     =         20
Logistic regression                               Number of obs   =     423001
                                                  Average RVI     =     0.0231
                                                  Largest FMI     =     0.1773
DF adjustment:   Large sample                     DF:     min     =     622.97
                                                          avg     =   1.35e+08
                                                          max     =   1.04e+09
Model F test:       Equal FMI                     F(  11,346557.9)=     171.95
Within VCE type:       Robust                     Prob > F        =     0.0000

                            (Within VCE adjusted for 13538 clusters in dyadid)
------------------------------------------------------------------------------
     midonsl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       CIElc |  -.0352853   .0606544    -0.58   0.561    -.1543972    .0838267
         bdm |  -.6765676   .1975998    -3.42   0.001    -1.063876   -.2892597
    bdmCIElc |   -.464799   .1155888    -4.02   0.000    -.6913865   -.2382114
      lncprt |  -.2483365   .0607326    -4.09   0.000    -.3673702   -.1293029
        mjpw |   2.048622   .2403511     8.52   0.000     1.577543    2.519702
     contigl |  -4.369686   .7313151    -5.98   0.000    -5.803039   -2.936333
      lndist |  -.9727917   .0996038    -9.77   0.000    -1.168012   -.7775718
    numstate |   .0055846    .001328     4.21   0.000     .0029814    .0081878
    midyears |  -.4105718   .0280809   -14.62   0.000    -.4656094   -.3555342
   midyears2 |   .0160715   .0015589    10.31   0.000     .0130161    .0191269
   midyears3 |  -.0001819   .0000238    -7.65   0.000    -.0002286   -.0001353
       _cons |   2.557009   .7364722     3.47   0.001      1.11355    4.000468
------------------------------------------------------------------------------
(1 real change made)
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(1 real change made)
(1 real change made)
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Imputations (20):
  .........10.........20 done

Multiple-imputation estimates                     Imputations     =         20
Logistic regression                               Number of obs   =     423726
                                                  Average RVI     =     0.0381
                                                  Largest FMI     =     0.2593
DF adjustment:   Large sample                     DF:     min     =     293.92
                                                          avg     =   7.83e+07
                                                          max     =   4.23e+08
Model F test:       Equal FMI                     F(  11,134758.5)=     175.10
Within VCE type:       Robust                     Prob > F        =     0.0000

                            (Within VCE adjusted for 13538 clusters in dyadid)
------------------------------------------------------------------------------
     midongl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       CIElc |  -.0627086   .0594221    -1.06   0.292    -.1796554    .0542381
         bdm |   -.914955   .2099009    -4.36   0.000    -1.326373   -.5035372
    bdmCIElc |  -.4843924   .1100492    -4.40   0.000    -.7001901   -.2685946
      lncprt |  -.2762612   .0513447    -5.38   0.000     -.376895   -.1756274
        mjpw |    2.24981   .2183074    10.31   0.000     1.821935    2.677684
     contigl |   -3.53442   .7083953    -4.99   0.000    -4.922852   -2.145988
      lndist |  -.8281043   .0935548    -8.85   0.000    -1.011469   -.6447401
    numstate |   .0061445   .0012056     5.10   0.000     .0037803    .0085086
    midyears |  -.5818032   .0279524   -20.81   0.000     -.636589   -.5270174
   midyears2 |   .0247239   .0015835    15.61   0.000     .0216202    .0278275
   midyears3 |  -.0002988   .0000248   -12.03   0.000    -.0003475   -.0002502
       _cons |   2.232503   .7079021     3.15   0.002     .8450397    3.619966
------------------------------------------------------------------------------
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)

Imputations (20):
  .........10.........20 done

Multiple-imputation estimates                     Imputations     =         20
Logistic regression                               Number of obs   =     422735
                                                  Average RVI     =     0.0791
                                                  Largest FMI     =     0.2206
DF adjustment:   Large sample                     DF:     min     =     404.57
                                                          avg     =   7.21e+07
                                                          max     =   4.70e+08
Model F test:       Equal FMI                     F(  11,39213.4) =      62.85
Within VCE type:       Robust                     Prob > F        =     0.0000

                            (Within VCE adjusted for 13538 clusters in dyadid)
------------------------------------------------------------------------------
    fmidonsl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       CIElc |  -.3834411   .1533628    -2.50   0.013    -.6849286   -.0819536
         bdm |  -1.261272   .6665051    -1.89   0.058     -2.56763    .0450848
    bdmCIElc |  -1.006398   .2440346    -4.12   0.000    -1.485742   -.5270541
      lncprt |  -.3764907   .1159014    -3.25   0.001    -.6036534   -.1493279
        mjpw |   1.664305   .4844158     3.44   0.001     .7148671    2.613743
     contigl |   3.022031   2.892861     1.04   0.296    -2.647872    8.691933
      lndist |  -.2004083   .3669294    -0.55   0.585    -.9195767    .5187601
    numstate |   .0114352    .003681     3.11   0.002     .0042184    .0186521
   fmidyears |  -.4555273   .0742678    -6.13   0.000    -.6010896   -.3099651
  fmidyears2 |   .0154439   .0042907     3.60   0.000     .0070344    .0238535
  fmidyears3 |  -.0001467   .0000657    -2.23   0.026    -.0002755   -.0000179
       _cons |  -7.207927    2.86952    -2.51   0.012     -12.8321   -1.583758
------------------------------------------------------------------------------
(1 real change made)
(1 real change made)
(1 real change made)
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(1 real change made)
(1 real change made)
specification was str4 now str5
(1 real change made)

Imputations (20):
  .........10.........20 done

Multiple-imputation estimates                     Imputations     =         20
Logistic regression                               Number of obs   =     423430
                                                  Average RVI     =     0.1589
                                                  Largest FMI     =     0.3777
DF adjustment:   Large sample                     DF:     min     =     139.64
                                                          avg     = 6765362.89
                                                          max     =   3.12e+07
Model F test:       Equal FMI                     F(  11,10537.6) =      57.21
Within VCE type:       Robust                     Prob > F        =     0.0000

                            (Within VCE adjusted for 13538 clusters in dyadid)
------------------------------------------------------------------------------
    fmidongl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       CIElc |  -.3052406   .0986288    -3.09   0.002    -.5002395   -.1102417
         bdm |   -1.46524    .569851    -2.57   0.010    -2.582141   -.3483393
    bdmCIElc |  -.8343264    .172423    -4.84   0.000    -1.174443   -.4942101
      lncprt |  -.3749734   .0769233    -4.87   0.000    -.5257404   -.2242065
        mjpw |   2.282961    .355796     6.42   0.000     1.585613    2.980308
     contigl |    -1.2551   1.382702    -0.91   0.364    -3.965146    1.454947
      lndist |  -.5679292   .1763034    -3.22   0.001    -.9134776   -.2223808
    numstate |   .0049746   .0021484     2.32   0.021     .0007599    .0091893
   fmidyears |  -.6547099   .0446482   -14.66   0.000    -.7422189    -.567201
  fmidyears2 |   .0266875   .0023013    11.60   0.000     .0221771     .031198
  fmidyears3 |  -.0002991   .0000331    -9.03   0.000     -.000364   -.0002342
       _cons |   -.658481   1.385875    -0.48   0.635    -3.374758    2.057796
------------------------------------------------------------------------------
(1 real change made)
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Imputations (20):
  .........10.........20 done

Multiple-imputation estimates                     Imputations     =         20
Logistic regression                               Number of obs   =     423001
                                                  Average RVI     =     0.0252
                                                  Largest FMI     =     0.1495
DF adjustment:   Large sample                     DF:     min     =     873.25
                                                          avg     =   6.91e+07
                                                          max     =   7.38e+08
Model F test:       Equal FMI                     F(  11,474881.9)=     180.37
Within VCE type:       Robust                     Prob > F        =     0.0000

                            (Within VCE adjusted for 13538 clusters in dyadid)
------------------------------------------------------------------------------
     midonsl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        CIEl |  -.1634971    .062009    -2.64   0.009    -.2852012   -.0417931
         bdm |  -1.145189   .2571498    -4.45   0.000    -1.649214   -.6411644
         dmh |   .0474364   .0097855     4.85   0.000     .0282571    .0666158
      lncprt |  -.2350871   .0586657    -4.01   0.000    -.3500697   -.1201045
        mjpw |   1.932967   .2297954     8.41   0.000     1.482577    2.383358
     contigl |  -4.421043   .7237401    -6.11   0.000    -5.839548   -3.002537
      lndist |  -.9957226     .09761   -10.20   0.000    -1.187035   -.8044104
    numstate |   .0062176   .0013187     4.72   0.000     .0036326    .0088026
    midyears |  -.3971426   .0276539   -14.36   0.000    -.4513432   -.3429421
   midyears2 |    .015505   .0015463    10.03   0.000     .0124744    .0185357
   midyears3 |   -.000177   .0000238    -7.44   0.000    -.0002237   -.0001304
       _cons |   2.670188    .728319     3.67   0.000     1.242709    4.097668
------------------------------------------------------------------------------
(1 real change made)
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Imputations (20):
  .........10.........20 done

Multiple-imputation estimates                     Imputations     =         20
Logistic regression                               Number of obs   =     423726
                                                  Average RVI     =     0.0408
                                                  Largest FMI     =     0.2183
DF adjustment:   Large sample                     DF:     min     =     412.82
                                                          avg     =   3.09e+07
                                                          max     =   2.89e+08
Model F test:       Equal FMI                     F(  11,171829.2)=     184.69
Within VCE type:       Robust                     Prob > F        =     0.0000

                            (Within VCE adjusted for 13538 clusters in dyadid)
------------------------------------------------------------------------------
     midongl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        CIEl |  -.1742734   .0609577    -2.86   0.004    -.2940996   -.0544471
         bdm |  -1.286097   .2669261    -4.82   0.000    -1.809287   -.7629084
         dmh |   .0398533   .0093183     4.28   0.000     .0215897    .0581169
      lncprt |  -.2638354   .0501479    -5.26   0.000    -.3621233   -.1655474
        mjpw |   2.128607    .210882    10.09   0.000     1.715285    2.541928
     contigl |   -3.59751   .6968599    -5.16   0.000    -4.963333   -2.231688
      lndist |   -.849693    .090952    -9.34   0.000    -1.027956   -.6714301
    numstate |   .0066382   .0012018     5.52   0.000     .0042817    .0089948
    midyears |  -.5713854   .0278123   -20.54   0.000    -.6258964   -.5168743
   midyears2 |   .0243056   .0015831    15.35   0.000     .0212028    .0274085
   midyears3 |  -.0002957    .000025   -11.84   0.000    -.0003446   -.0002467
       _cons |    2.43406   .6969002     3.49   0.000      1.06816    3.799961
------------------------------------------------------------------------------
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Imputations (20):
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Multiple-imputation estimates                     Imputations     =         20
Logistic regression                               Number of obs   =     422735
                                                  Average RVI     =     0.0491
                                                  Largest FMI     =     0.1638
DF adjustment:   Large sample                     DF:     min     =     728.23
                                                          avg     =   4.01e+07
                                                          max     =   2.60e+08
Model F test:       Equal FMI                     F(  11,129877.7)=      61.18
Within VCE type:       Robust                     Prob > F        =     0.0000

                            (Within VCE adjusted for 13538 clusters in dyadid)
------------------------------------------------------------------------------
    fmidonsl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        CIEl |  -.5123698   .1511133    -3.39   0.001    -.8090396      -.2157
         bdm |  -.8457197   .5968359    -1.42   0.156    -2.015522    .3240831
         dmh |   .0464907   .0262781     1.77   0.077    -.0050134    .0979948
      lncprt |  -.3564354   .1117681    -3.19   0.001    -.5754969    -.137374
        mjpw |   1.606012   .4771677     3.37   0.001     .6707788    2.541245
     contigl |   2.859653    2.77799     1.03   0.303    -2.585108    8.304414
      lndist |  -.2333489   .3507068    -0.67   0.506    -.9207216    .4540238
    numstate |   .0122085   .0035968     3.39   0.001     .0051568    .0192602
   fmidyears |  -.4435999   .0750159    -5.91   0.000    -.5906284   -.2965714
  fmidyears2 |   .0150153   .0043346     3.46   0.001     .0065196    .0235109
  fmidyears3 |  -.0001442   .0000665    -2.17   0.030    -.0002746   -.0000139
       _cons |  -6.313141   2.740289    -2.30   0.021    -11.68401   -.9422718
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Imputations (20):
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Multiple-imputation estimates                     Imputations     =         20
Logistic regression                               Number of obs   =     423430
                                                  Average RVI     =     0.1083
                                                  Largest FMI     =     0.3274
DF adjustment:   Large sample                     DF:     min     =     185.39
                                                          avg     = 3264173.63
                                                          max     =   1.36e+07
Model F test:       Equal FMI                     F(  11,27090.2) =      62.79
Within VCE type:       Robust                     Prob > F        =     0.0000

                            (Within VCE adjusted for 13538 clusters in dyadid)
------------------------------------------------------------------------------
    fmidongl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        CIEl |  -.4224913   .0966972    -4.37   0.000    -.6132596    -.231723
         bdm |  -1.208277   .5209061    -2.32   0.020    -2.229254   -.1872987
         dmh |   .0421431   .0152633     2.76   0.006     .0122273     .072059
      lncprt |  -.3591565   .0748713    -4.80   0.000    -.5059016   -.2124115
        mjpw |   2.178119   .3382455     6.44   0.000      1.51517    2.841068
     contigl |  -1.301071   1.336883    -0.97   0.330    -3.921316    1.319174
      lndist |  -.5880638   .1670259    -3.52   0.000    -.9154288   -.2606988
    numstate |    .005474   .0020821     2.63   0.009     .0013893    .0095587
   fmidyears |  -.6442355   .0441372   -14.60   0.000    -.7307428   -.5577282
  fmidyears2 |   .0262999   .0022854    11.51   0.000     .0218207    .0307792
  fmidyears3 |  -.0002966    .000033    -8.98   0.000    -.0003614   -.0002319
       _cons |   .0179984   1.342154     0.01   0.989    -2.612577    2.648574
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Imputations (20):
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Multiple-imputation estimates                     Imputations     =         20
Logistic regression                               Number of obs   =     423001
                                                  Average RVI     =     0.0232
                                                  Largest FMI     =     0.1787
DF adjustment:   Large sample                     DF:     min     =     612.99
                                                          avg     =   2.83e+07
                                                          max     =   2.24e+08
Model F test:       Equal FMI                     F(  12,435531.9)=     161.85
Within VCE type:       Robust                     Prob > F        =     0.0000

                            (Within VCE adjusted for 13538 clusters in dyadid)
------------------------------------------------------------------------------
     midonsl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       CIElc |  -.0950002    .062422    -1.52   0.129    -.2175871    .0275866
         bdm |  -.9149431   .1892798    -4.83   0.000    -1.285935   -.5439508
    bdmCIElc |  -.4129764   .1144624    -3.61   0.000    -.6373591   -.1885936
         dmh |   .0450065   .0097411     4.62   0.000     .0259141    .0640988
      lncprt |  -.2446043    .057981    -4.22   0.000     -.358245   -.1309636
        mjpw |   1.979544   .2259589     8.76   0.000     1.536673    2.422415
     contigl |    -4.4094   .7241373    -6.09   0.000    -5.828685   -2.990116
      lndist |  -.9971309    .097481   -10.23   0.000     -1.18819   -.8060716
    numstate |   .0056515   .0013027     4.34   0.000     .0030977    .0082053
    midyears |  -.3957564   .0275956   -14.34   0.000    -.4498429     -.34167
   midyears2 |   .0154466   .0015426    10.01   0.000     .0124232    .0184701
   midyears3 |   -.000176   .0000237    -7.41   0.000    -.0002226   -.0001295
       _cons |   2.468254   .7292513     3.38   0.001     1.038947    3.897561
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Imputations (20):
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Multiple-imputation estimates                     Imputations     =         20
Logistic regression                               Number of obs   =     423726
                                                  Average RVI     =     0.0416
                                                  Largest FMI     =     0.2732
DF adjustment:   Large sample                     DF:     min     =     265.01
                                                          avg     =   1.68e+07
                                                          max     =   8.13e+07
Model F test:       Equal FMI                     F(  12,138901.9)=     168.99
Within VCE type:       Robust                     Prob > F        =     0.0000

                            (Within VCE adjusted for 13538 clusters in dyadid)
------------------------------------------------------------------------------
     midongl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       CIElc |  -.1112569   .0606648    -1.83   0.068    -.2307031    .0081894
         bdm |  -1.108702   .2009099    -5.52   0.000     -1.50249   -.7149145
    bdmCIElc |  -.4390561   .1091458    -4.02   0.000    -.6530976   -.2250146
         dmh |   .0374969   .0092155     4.07   0.000     .0194346    .0555592
      lncprt |  -.2718071   .0492424    -5.52   0.000    -.3683205   -.1752938
        mjpw |   2.172363   .2071012    10.49   0.000     1.766453    2.578274
     contigl |  -3.591427   .6995187    -5.13   0.000    -4.962462   -2.220393
      lndist |  -.8512505   .0910865    -9.35   0.000    -1.029777   -.6727241
    numstate |   .0061391   .0011797     5.20   0.000     .0038257    .0084526
    midyears |  -.5696309   .0277146   -20.55   0.000    -.6239505   -.5153114
   midyears2 |   .0242193   .0015768    15.36   0.000     .0211287    .0273098
   midyears3 |  -.0002942   .0000249   -11.83   0.000     -.000343   -.0002455
       _cons |   2.198663   .7005936     3.14   0.002     .8255239    3.571803
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Imputations (20):
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Multiple-imputation estimates                     Imputations     =         20
Logistic regression                               Number of obs   =     422735
                                                  Average RVI     =     0.0779
                                                  Largest FMI     =     0.2055
DF adjustment:   Large sample                     DF:     min     =     465.38
                                                          avg     =   2.68e+07
                                                          max     =   1.56e+08
Model F test:       Equal FMI                     F(  12,45389.3) =      53.84
Within VCE type:       Robust                     Prob > F        =     0.0000

                            (Within VCE adjusted for 13538 clusters in dyadid)
------------------------------------------------------------------------------
    fmidonsl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       CIElc |  -.4216666   .1493314    -2.82   0.005    -.7151139   -.1282194
         bdm |  -1.540727   .6247417    -2.47   0.014    -2.765221   -.3162323
    bdmCIElc |  -.9768811   .2413502    -4.05   0.000    -1.450852   -.5029103
         dmh |   .0454997   .0261275     1.74   0.082    -.0057092    .0967086
      lncprt |  -.3729167   .1119107    -3.33   0.001    -.5922579   -.1535755
        mjpw |   1.679547   .4725867     3.55   0.000     .7532937    2.605801
     contigl |   2.910492   2.830127     1.03   0.304    -2.636456     8.45744
      lndist |  -.2304967   .3569842    -0.65   0.518     -.930173    .4691795
    numstate |   .0116846   .0036406     3.21   0.001     .0045467    .0188225
   fmidyears |  -.4437976   .0752019    -5.90   0.000    -.5911907   -.2964045
  fmidyears2 |   .0150234   .0043669     3.44   0.001     .0064644    .0235824
  fmidyears3 |  -.0001443   .0000672    -2.15   0.032    -.0002761   -.0000125
       _cons |  -7.251647   2.822387    -2.57   0.010    -12.78344   -1.719856
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Imputations (20):
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Multiple-imputation estimates                     Imputations     =         20
Logistic regression                               Number of obs   =     423430
                                                  Average RVI     =     0.1637
                                                  Largest FMI     =     0.3921
DF adjustment:   Large sample                     DF:     min     =     129.64
                                                          avg     = 3120588.84
                                                          max     =   1.77e+07
Model F test:       Equal FMI                     F(  12,11345.5) =      55.93
Within VCE type:       Robust                     Prob > F        =     0.0000

                            (Within VCE adjusted for 13538 clusters in dyadid)
------------------------------------------------------------------------------
    fmidongl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       CIElc |  -.3469117   .0968585    -3.58   0.000    -.5385396   -.1552838
         bdm |  -1.675521   .5524089    -3.03   0.002    -2.758232   -.5928112
    bdmCIElc |  -.7974949   .1707517    -4.67   0.000    -1.134218    -.460772
         dmh |   .0403357   .0151488     2.66   0.008     .0106442    .0700272
      lncprt |  -.3680786   .0740303    -4.97   0.000    -.5131753   -.2229819
        mjpw |   2.227706   .3324933     6.70   0.000     1.576031    2.879381
     contigl |  -1.308572   1.361696    -0.96   0.337    -3.977448    1.360305
      lndist |  -.5910758   .1699935    -3.48   0.001     -.924257   -.2578946
    numstate |   .0050961   .0020761     2.45   0.014     .0010224    .0091698
   fmidyears |  -.6435531   .0442756   -14.54   0.000    -.7303316   -.5567746
  fmidyears2 |   .0262601   .0022983    11.43   0.000     .0217555    .0307647
  fmidyears3 |   -.000296   .0000333    -8.89   0.000    -.0003613   -.0002308
       _cons |  -.7214137   1.380562    -0.52   0.601    -3.427279    1.984451
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Imputations (20):
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Multiple-imputation estimates                     Imputations     =         20
Logistic regression                               Number of obs   =     423001
                                                  Average RVI     =     0.0205
                                                  Largest FMI     =     0.0772
DF adjustment:   Large sample                     DF:     min     =    3234.27
                                                          avg     =   4.22e+07
                                                          max     =   2.19e+08
Model F test:       Equal FMI                     F(  10, 1.1e+06)=     192.14
Within VCE type:       Robust                     Prob > F        =     0.0000

                            (Within VCE adjusted for 13538 clusters in dyadid)
------------------------------------------------------------------------------
     midonsl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  lnlifepenl |  -.2161453   .0531357    -4.07   0.000    -.3203283   -.1119623
         bdm |   -.918216   .2363206    -3.89   0.000    -1.381398   -.4550338
      lncprt |  -.2325377   .0612996    -3.79   0.000    -.3526827   -.1123927
        mjpw |   2.018096   .2462099     8.20   0.000     1.535533    2.500658
     contigl |  -4.302252    .728812    -5.90   0.000    -5.730697   -2.873806
      lndist |  -.9561442   .0992527    -9.63   0.000    -1.150676   -.7616126
    numstate |   .0065741   .0013082     5.03   0.000     .0040101    .0091381
    midyears |  -.4112557   .0281509   -14.61   0.000    -.4664304   -.3560809
   midyears2 |   .0160368   .0015594    10.28   0.000     .0129805    .0190931
   midyears3 |  -.0001812   .0000237    -7.63   0.000    -.0002278   -.0001347
       _cons |   .9336701   .8935807     1.04   0.296    -.8177352    2.685075
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Imputations (20):
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Multiple-imputation estimates                     Imputations     =         20
Logistic regression                               Number of obs   =     423726
                                                  Average RVI     =     0.0286
                                                  Largest FMI     =     0.1389
DF adjustment:   Large sample                     DF:     min     =    1009.46
                                                          avg     =   2.18e+07
                                                          max     =   5.55e+07
Model F test:       Equal FMI                     F(  10,416756.6)=     193.72
Within VCE type:       Robust                     Prob > F        =     0.0000

                            (Within VCE adjusted for 13538 clusters in dyadid)
------------------------------------------------------------------------------
     midongl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  lnlifepenl |  -.2046795   .0511611    -4.00   0.000    -.3050738   -.1042852
         bdm |  -1.142022    .257649    -4.43   0.000    -1.647008   -.6370347
      lncprt |  -.2616862   .0518127    -5.05   0.000    -.3632372   -.1601351
        mjpw |   2.207693    .219355    10.06   0.000     1.777765    2.637621
     contigl |   -3.41402   .7047823    -4.84   0.000    -4.795368   -2.032672
      lndist |  -.8059341   .0931802    -8.65   0.000     -.988564   -.6233043
    numstate |   .0067003   .0011559     5.80   0.000     .0044347    .0089658
    midyears |  -.5835351    .028123   -20.75   0.000    -.6386552   -.5284151
   midyears2 |   .0247555   .0015889    15.58   0.000     .0216413    .0278697
   midyears3 |   -.000299   .0000248   -12.04   0.000    -.0003477   -.0002503
       _cons |   .7271844   .8391317     0.87   0.386     -.917556    2.371925
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Imputations (20):
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Multiple-imputation estimates                     Imputations     =         20
Logistic regression                               Number of obs   =     422735
                                                  Average RVI     =     0.0153
                                                  Largest FMI     =     0.0975
DF adjustment:   Large sample                     DF:     min     =    2035.79
                                                          avg     =   8.92e+08
                                                          max     =   7.25e+09
Model F test:       Equal FMI                     F(  10,857137.1)=      97.74
Within VCE type:       Robust                     Prob > F        =     0.0000

                            (Within VCE adjusted for 13538 clusters in dyadid)
------------------------------------------------------------------------------
    fmidonsl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  lnlifepenl |  -.2800623     .10436    -2.68   0.007    -.4847257   -.0753989
         bdm |  -1.119337   .6069149    -1.84   0.065    -2.308872    .0701979
      lncprt |  -.3754196   .1105521    -3.40   0.001    -.5920978   -.1587414
        mjpw |    1.65832   .4804779     3.45   0.001     .7166003    2.600039
     contigl |   3.398374   2.740877     1.24   0.215    -1.973647    8.770395
      lndist |  -.1498289   .3495065    -0.43   0.668     -.834849    .5351913
    numstate |   .0086263   .0028361     3.04   0.002     .0030676     .014185
   fmidyears |  -.4589903   .0742452    -6.18   0.000    -.6045083   -.3134723
  fmidyears2 |   .0156744   .0042885     3.65   0.000     .0072691    .0240796
  fmidyears3 |  -.0001502   .0000656    -2.29   0.022    -.0002787   -.0000217
       _cons |  -8.596051    2.88218    -2.98   0.003    -14.24506   -2.947044
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Imputations (20):
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Multiple-imputation estimates                     Imputations     =         20
Logistic regression                               Number of obs   =     423430
                                                  Average RVI     =     0.0589
                                                  Largest FMI     =     0.2992
DF adjustment:   Large sample                     DF:     min     =     221.53
                                                          avg     =   8.53e+07
                                                          max     =   4.95e+08
Model F test:       Equal FMI                     F(  10,59831.0) =      81.58
Within VCE type:       Robust                     Prob > F        =     0.0000

                            (Within VCE adjusted for 13538 clusters in dyadid)
------------------------------------------------------------------------------
    fmidongl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  lnlifepenl |  -.2691323   .0651093    -4.13   0.000    -.3974453   -.1408194
         bdm |  -1.385225   .5406564    -2.56   0.010    -2.444897   -.3255524
      lncprt |  -.3618326   .0749976    -4.82   0.000    -.5088253     -.21484
        mjpw |   2.216578    .354422     6.25   0.000     1.521924    2.911233
     contigl |  -.7925831   1.351488    -0.59   0.558     -3.44145    1.856284
      lndist |  -.5059609   .1734521    -2.92   0.004    -.8459208    -.166001
    numstate |   .0030954   .0017172     1.80   0.071    -.0002707    .0064614
   fmidyears |  -.6565062    .044602   -14.72   0.000    -.7439244   -.5690879
  fmidyears2 |   .0268093   .0022998    11.66   0.000     .0223018    .0313168
  fmidyears3 |  -.0003012    .000033    -9.12   0.000    -.0003659   -.0002365
       _cons |   -2.31032   1.524103    -1.52   0.130     -5.29774    .6770991
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Imputations (20):
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Multiple-imputation estimates                     Imputations     =         20
Logistic regression                               Number of obs   =     423001
                                                  Average RVI     =     0.0231
                                                  Largest FMI     =     0.1773
DF adjustment:   Large sample                     DF:     min     =     622.97
                                                          avg     =   1.35e+08
                                                          max     =   1.04e+09
Model F test:       Equal FMI                     F(  11,346557.9)=     171.95
Within VCE type:       Robust                     Prob > F        =     0.0000

                            (Within VCE adjusted for 13538 clusters in dyadid)
------------------------------------------------------------------------------
     midonsl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       CIElc |  -.0352853   .0606544    -0.58   0.561    -.1543972    .0838267
         bdm |  -.6765676   .1975998    -3.42   0.001    -1.063876   -.2892597
    bdmCIElc |   -.464799   .1155888    -4.02   0.000    -.6913865   -.2382114
      lncprt |  -.2483365   .0607326    -4.09   0.000    -.3673702   -.1293029
        mjpw |   2.048622   .2403511     8.52   0.000     1.577543    2.519702
     contigl |  -4.369686   .7313151    -5.98   0.000    -5.803039   -2.936333
      lndist |  -.9727917   .0996038    -9.77   0.000    -1.168012   -.7775718
    numstate |   .0055846    .001328     4.21   0.000     .0029814    .0081878
    midyears |  -.4105718   .0280809   -14.62   0.000    -.4656094   -.3555342
   midyears2 |   .0160715   .0015589    10.31   0.000     .0130161    .0191269
   midyears3 |  -.0001819   .0000238    -7.65   0.000    -.0002286   -.0001353
       _cons |   2.557009   .7364722     3.47   0.001      1.11355    4.000468
------------------------------------------------------------------------------
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Imputations (20):
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Multiple-imputation estimates                     Imputations     =         20
Logistic regression                               Number of obs   =     423726
                                                  Average RVI     =     0.0381
                                                  Largest FMI     =     0.2593
DF adjustment:   Large sample                     DF:     min     =     293.92
                                                          avg     =   7.83e+07
                                                          max     =   4.23e+08
Model F test:       Equal FMI                     F(  11,134758.5)=     175.10
Within VCE type:       Robust                     Prob > F        =     0.0000

                            (Within VCE adjusted for 13538 clusters in dyadid)
------------------------------------------------------------------------------
     midongl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       CIElc |  -.0627086   .0594221    -1.06   0.292    -.1796554    .0542381
         bdm |   -.914955   .2099009    -4.36   0.000    -1.326373   -.5035372
    bdmCIElc |  -.4843924   .1100492    -4.40   0.000    -.7001901   -.2685946
      lncprt |  -.2762612   .0513447    -5.38   0.000     -.376895   -.1756274
        mjpw |    2.24981   .2183074    10.31   0.000     1.821935    2.677684
     contigl |   -3.53442   .7083953    -4.99   0.000    -4.922852   -2.145988
      lndist |  -.8281043   .0935548    -8.85   0.000    -1.011469   -.6447401
    numstate |   .0061445   .0012056     5.10   0.000     .0037803    .0085086
    midyears |  -.5818032   .0279524   -20.81   0.000     -.636589   -.5270174
   midyears2 |   .0247239   .0015835    15.61   0.000     .0216202    .0278275
   midyears3 |  -.0002988   .0000248   -12.03   0.000    -.0003475   -.0002502
       _cons |   2.232503   .7079021     3.15   0.002     .8450397    3.619966
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Imputations (20):
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Multiple-imputation estimates                     Imputations     =         20
Logistic regression                               Number of obs   =     422735
                                                  Average RVI     =     0.0791
                                                  Largest FMI     =     0.2206
DF adjustment:   Large sample                     DF:     min     =     404.57
                                                          avg     =   7.21e+07
                                                          max     =   4.70e+08
Model F test:       Equal FMI                     F(  11,39213.4) =      62.85
Within VCE type:       Robust                     Prob > F        =     0.0000

                            (Within VCE adjusted for 13538 clusters in dyadid)
------------------------------------------------------------------------------
    fmidonsl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       CIElc |  -.3834411   .1533628    -2.50   0.013    -.6849286   -.0819536
         bdm |  -1.261272   .6665051    -1.89   0.058     -2.56763    .0450848
    bdmCIElc |  -1.006398   .2440346    -4.12   0.000    -1.485742   -.5270541
      lncprt |  -.3764907   .1159014    -3.25   0.001    -.6036534   -.1493279
        mjpw |   1.664305   .4844158     3.44   0.001     .7148671    2.613743
     contigl |   3.022031   2.892861     1.04   0.296    -2.647872    8.691933
      lndist |  -.2004083   .3669294    -0.55   0.585    -.9195767    .5187601
    numstate |   .0114352    .003681     3.11   0.002     .0042184    .0186521
   fmidyears |  -.4555273   .0742678    -6.13   0.000    -.6010896   -.3099651
  fmidyears2 |   .0154439   .0042907     3.60   0.000     .0070344    .0238535
  fmidyears3 |  -.0001467   .0000657    -2.23   0.026    -.0002755   -.0000179
       _cons |  -7.207927    2.86952    -2.51   0.012     -12.8321   -1.583758
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Imputations (20):
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Multiple-imputation estimates                     Imputations     =         20
Logistic regression                               Number of obs   =     423430
                                                  Average RVI     =     0.1589
                                                  Largest FMI     =     0.3777
DF adjustment:   Large sample                     DF:     min     =     139.64
                                                          avg     = 6765362.89
                                                          max     =   3.12e+07
Model F test:       Equal FMI                     F(  11,10537.6) =      57.21
Within VCE type:       Robust                     Prob > F        =     0.0000

                            (Within VCE adjusted for 13538 clusters in dyadid)
------------------------------------------------------------------------------
    fmidongl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       CIElc |  -.3052406   .0986288    -3.09   0.002    -.5002395   -.1102417
         bdm |   -1.46524    .569851    -2.57   0.010    -2.582141   -.3483393
    bdmCIElc |  -.8343264    .172423    -4.84   0.000    -1.174443   -.4942101
      lncprt |  -.3749734   .0769233    -4.87   0.000    -.5257404   -.2242065
        mjpw |   2.282961    .355796     6.42   0.000     1.585613    2.980308
     contigl |    -1.2551   1.382702    -0.91   0.364    -3.965146    1.454947
      lndist |  -.5679292   .1763034    -3.22   0.001    -.9134776   -.2223808
    numstate |   .0049746   .0021484     2.32   0.021     .0007599    .0091893
   fmidyears |  -.6547099   .0446482   -14.66   0.000    -.7422189    -.567201
  fmidyears2 |   .0266875   .0023013    11.60   0.000     .0221771     .031198
  fmidyears3 |  -.0002991   .0000331    -9.03   0.000     -.000364   -.0002342
       _cons |   -.658481   1.385875    -0.48   0.635    -3.374758    2.057796
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Imputations (20):
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Multiple-imputation estimates                     Imputations     =         20
Logistic regression                               Number of obs   =     423001
                                                  Average RVI     =     0.0245
                                                  Largest FMI     =     0.1011
DF adjustment:   Large sample                     DF:     min     =    1894.49
                                                          avg     =   1.49e+07
                                                          max     =   7.33e+07
Model F test:       Equal FMI                     F(  11,768717.0)=     187.56
Within VCE type:       Robust                     Prob > F        =     0.0000

                            (Within VCE adjusted for 13538 clusters in dyadid)
------------------------------------------------------------------------------
     midonsl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  lnlifepenl |  -.2475265   .0477822    -5.18   0.000    -.3412378   -.1538151
         bdm |   -1.18141   .2229509    -5.30   0.000    -1.618388   -.7444318
         dmh |   .0467387   .0091999     5.08   0.000     .0287072    .0647703
      lncprt |  -.2278785   .0578666    -3.94   0.000     -.341295    -.114462
        mjpw |    1.93616    .226864     8.53   0.000     1.491514    2.380805
     contigl |  -4.265975   .7198371    -5.93   0.000     -5.67683    -2.85512
      lndist |  -.9722542   .0967413   -10.05   0.000    -1.161864   -.7826447
    numstate |   .0062341   .0012785     4.88   0.000     .0037282      .00874
    midyears |   -.396593   .0277306   -14.30   0.000     -.450944    -.342242
   midyears2 |   .0154205    .001545     9.98   0.000     .0123923    .0184487
   midyears3 |  -.0001753   .0000237    -7.40   0.000    -.0002217   -.0001289
       _cons |   .6799197    .866293     0.78   0.433    -1.018009    2.377849
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Imputations (20):
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Multiple-imputation estimates                     Imputations     =         20
Logistic regression                               Number of obs   =     423726
                                                  Average RVI     =     0.0350
                                                  Largest FMI     =     0.1836
DF adjustment:   Large sample                     DF:     min     =     581.19
                                                          avg     = 8110097.90
                                                          max     =   2.01e+07
Model F test:       Equal FMI                     F(  11,286991.8)=     189.27
Within VCE type:       Robust                     Prob > F        =     0.0000

                            (Within VCE adjusted for 13538 clusters in dyadid)
------------------------------------------------------------------------------
     midongl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  lnlifepenl |   -.231681   .0475092    -4.88   0.000    -.3249917   -.1383703
         bdm |  -1.352107   .2417735    -5.59   0.000    -1.825978   -.8782362
         dmh |   .0386062   .0089117     4.33   0.000     .0211395    .0560729
      lncprt |  -.2564006   .0494177    -5.19   0.000    -.3532575   -.1595437
        mjpw |   2.118509   .2064532    10.26   0.000     1.713868     2.52315
     contigl |  -3.416082   .6954744    -4.91   0.000    -4.779187   -2.052977
      lndist |  -.8231194   .0905775    -9.09   0.000    -1.000648   -.6455907
    numstate |   .0063585    .001134     5.61   0.000     .0041358    .0085813
    midyears |  -.5715671    .027928   -20.47   0.000    -.6263051   -.5168292
   midyears2 |    .024262   .0015833    15.32   0.000     .0211589    .0273651
   midyears3 |  -.0002944   .0000248   -11.85   0.000    -.0003431   -.0002457
       _cons |   .5585823   .8211642     0.68   0.496     -1.05097    2.168135
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Imputations (20):
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Multiple-imputation estimates                     Imputations     =         20
Logistic regression                               Number of obs   =     422735
                                                  Average RVI     =     0.0199
                                                  Largest FMI     =     0.1204
DF adjustment:   Large sample                     DF:     min     =    1340.59
                                                          avg     =   8.48e+08
                                                          max     =   7.56e+09
Model F test:       Equal FMI                     F(  11,547737.2)=      91.85
Within VCE type:       Robust                     Prob > F        =     0.0000

                            (Within VCE adjusted for 13538 clusters in dyadid)
------------------------------------------------------------------------------
    fmidonsl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  lnlifepenl |  -.2862482   .0930129    -3.08   0.002     -.468715   -.1037815
         bdm |  -1.398736   .5732058    -2.44   0.015    -2.522202   -.2752692
         dmh |   .0371821   .0238125     1.56   0.118    -.0094896    .0838538
      lncprt |  -.3751228   .1075733    -3.49   0.000    -.5859626   -.1642829
        mjpw |   1.655645   .4667661     3.55   0.000     .7407998     2.57049
     contigl |    3.34361   2.684877     1.25   0.213    -1.918652    8.605873
      lndist |  -.1703978   .3406423    -0.50   0.617    -.8380445    .4972489
    numstate |   .0084446   .0027916     3.03   0.002     .0029731    .0139161
   fmidyears |  -.4500343   .0751192    -5.99   0.000    -.5972653   -.3028033
  fmidyears2 |   .0153745   .0043565     3.53   0.000     .0068358    .0239132
  fmidyears3 |  -.0001489   .0000669    -2.22   0.026    -.0002801   -.0000177
       _cons |  -8.607142   2.809623    -3.06   0.002    -14.11394   -3.100342
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Imputations (20):
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Multiple-imputation estimates                     Imputations     =         20
Logistic regression                               Number of obs   =     423430
                                                  Average RVI     =     0.0781
                                                  Largest FMI     =     0.3607
DF adjustment:   Large sample                     DF:     min     =     152.97
                                                          avg     =   4.12e+07
                                                          max     =   2.69e+08
Model F test:       Equal FMI                     F(  11,37493.2) =      82.41
Within VCE type:       Robust                     Prob > F        =     0.0000

                            (Within VCE adjusted for 13538 clusters in dyadid)
------------------------------------------------------------------------------
    fmidongl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  lnlifepenl |  -.2842035   .0608686    -4.67   0.000     -.404455    -.163952
         bdm |  -1.598452   .5191135    -3.08   0.002    -2.615899   -.5810042
         dmh |   .0347563    .014183     2.45   0.014     .0069581    .0625544
      lncprt |     -.3568   .0728193    -4.90   0.000    -.4995231   -.2140768
        mjpw |   2.160636   .3337536     6.47   0.000     1.506491    2.814781
     contigl |  -.7934423   1.341084    -0.59   0.554    -3.421919    1.835034
      lndist |  -.5203821   .1686877    -3.08   0.002    -.8510038   -.1897604
    numstate |   .0029222   .0016836     1.74   0.083    -.0003778    .0062223
   fmidyears |  -.6477996   .0442604   -14.64   0.000    -.7345484   -.5610508
  fmidyears2 |   .0264914   .0022974    11.53   0.000     .0219886    .0309943
  fmidyears3 |  -.0002992   .0000332    -9.01   0.000    -.0003643   -.0002341
       _cons |  -2.423878   1.520227    -1.59   0.111    -5.403726    .5559697
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Imputations (20):
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Multiple-imputation estimates                     Imputations     =         20
Logistic regression                               Number of obs   =     423001
                                                  Average RVI     =     0.0232
                                                  Largest FMI     =     0.1787
DF adjustment:   Large sample                     DF:     min     =     612.99
                                                          avg     =   2.83e+07
                                                          max     =   2.24e+08
Model F test:       Equal FMI                     F(  12,435531.9)=     161.85
Within VCE type:       Robust                     Prob > F        =     0.0000

                            (Within VCE adjusted for 13538 clusters in dyadid)
------------------------------------------------------------------------------
     midonsl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       CIElc |  -.0950002    .062422    -1.52   0.129    -.2175871    .0275866
         bdm |  -.9149431   .1892798    -4.83   0.000    -1.285935   -.5439508
    bdmCIElc |  -.4129764   .1144624    -3.61   0.000    -.6373591   -.1885936
         dmh |   .0450065   .0097411     4.62   0.000     .0259141    .0640988
      lncprt |  -.2446043    .057981    -4.22   0.000     -.358245   -.1309636
        mjpw |   1.979544   .2259589     8.76   0.000     1.536673    2.422415
     contigl |    -4.4094   .7241373    -6.09   0.000    -5.828685   -2.990116
      lndist |  -.9971309    .097481   -10.23   0.000     -1.18819   -.8060716
    numstate |   .0056515   .0013027     4.34   0.000     .0030977    .0082053
    midyears |  -.3957564   .0275956   -14.34   0.000    -.4498429     -.34167
   midyears2 |   .0154466   .0015426    10.01   0.000     .0124232    .0184701
   midyears3 |   -.000176   .0000237    -7.41   0.000    -.0002226   -.0001295
       _cons |   2.468254   .7292513     3.38   0.001     1.038947    3.897561
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Imputations (20):
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Multiple-imputation estimates                     Imputations     =         20
Logistic regression                               Number of obs   =     423726
                                                  Average RVI     =     0.0416
                                                  Largest FMI     =     0.2732
DF adjustment:   Large sample                     DF:     min     =     265.01
                                                          avg     =   1.68e+07
                                                          max     =   8.13e+07
Model F test:       Equal FMI                     F(  12,138901.9)=     168.99
Within VCE type:       Robust                     Prob > F        =     0.0000

                            (Within VCE adjusted for 13538 clusters in dyadid)
------------------------------------------------------------------------------
     midongl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       CIElc |  -.1112569   .0606648    -1.83   0.068    -.2307031    .0081894
         bdm |  -1.108702   .2009099    -5.52   0.000     -1.50249   -.7149145
    bdmCIElc |  -.4390561   .1091458    -4.02   0.000    -.6530976   -.2250146
         dmh |   .0374969   .0092155     4.07   0.000     .0194346    .0555592
      lncprt |  -.2718071   .0492424    -5.52   0.000    -.3683205   -.1752938
        mjpw |   2.172363   .2071012    10.49   0.000     1.766453    2.578274
     contigl |  -3.591427   .6995187    -5.13   0.000    -4.962462   -2.220393
      lndist |  -.8512505   .0910865    -9.35   0.000    -1.029777   -.6727241
    numstate |   .0061391   .0011797     5.20   0.000     .0038257    .0084526
    midyears |  -.5696309   .0277146   -20.55   0.000    -.6239505   -.5153114
   midyears2 |   .0242193   .0015768    15.36   0.000     .0211287    .0273098
   midyears3 |  -.0002942   .0000249   -11.83   0.000     -.000343   -.0002455
       _cons |   2.198663   .7005936     3.14   0.002     .8255239    3.571803
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Imputations (20):
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Multiple-imputation estimates                     Imputations     =         20
Logistic regression                               Number of obs   =     422735
                                                  Average RVI     =     0.0779
                                                  Largest FMI     =     0.2055
DF adjustment:   Large sample                     DF:     min     =     465.38
                                                          avg     =   2.68e+07
                                                          max     =   1.56e+08
Model F test:       Equal FMI                     F(  12,45389.3) =      53.84
Within VCE type:       Robust                     Prob > F        =     0.0000

                            (Within VCE adjusted for 13538 clusters in dyadid)
------------------------------------------------------------------------------
    fmidonsl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       CIElc |  -.4216666   .1493314    -2.82   0.005    -.7151139   -.1282194
         bdm |  -1.540727   .6247417    -2.47   0.014    -2.765221   -.3162323
    bdmCIElc |  -.9768811   .2413502    -4.05   0.000    -1.450852   -.5029103
         dmh |   .0454997   .0261275     1.74   0.082    -.0057092    .0967086
      lncprt |  -.3729167   .1119107    -3.33   0.001    -.5922579   -.1535755
        mjpw |   1.679547   .4725867     3.55   0.000     .7532937    2.605801
     contigl |   2.910492   2.830127     1.03   0.304    -2.636456     8.45744
      lndist |  -.2304967   .3569842    -0.65   0.518     -.930173    .4691795
    numstate |   .0116846   .0036406     3.21   0.001     .0045467    .0188225
   fmidyears |  -.4437976   .0752019    -5.90   0.000    -.5911907   -.2964045
  fmidyears2 |   .0150234   .0043669     3.44   0.001     .0064644    .0235824
  fmidyears3 |  -.0001443   .0000672    -2.15   0.032    -.0002761   -.0000125
       _cons |  -7.251647   2.822387    -2.57   0.010    -12.78344   -1.719856
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Imputations (20):
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Multiple-imputation estimates                     Imputations     =         20
Logistic regression                               Number of obs   =     423430
                                                  Average RVI     =     0.1637
                                                  Largest FMI     =     0.3921
DF adjustment:   Large sample                     DF:     min     =     129.64
                                                          avg     = 3120588.84
                                                          max     =   1.77e+07
Model F test:       Equal FMI                     F(  12,11345.5) =      55.93
Within VCE type:       Robust                     Prob > F        =     0.0000

                            (Within VCE adjusted for 13538 clusters in dyadid)
------------------------------------------------------------------------------
    fmidongl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       CIElc |  -.3469117   .0968585    -3.58   0.000    -.5385396   -.1552838
         bdm |  -1.675521   .5524089    -3.03   0.002    -2.758232   -.5928112
    bdmCIElc |  -.7974949   .1707517    -4.67   0.000    -1.134218    -.460772
         dmh |   .0403357   .0151488     2.66   0.008     .0106442    .0700272
      lncprt |  -.3680786   .0740303    -4.97   0.000    -.5131753   -.2229819
        mjpw |   2.227706   .3324933     6.70   0.000     1.576031    2.879381
     contigl |  -1.308572   1.361696    -0.96   0.337    -3.977448    1.360305
      lndist |  -.5910758   .1699935    -3.48   0.001     -.924257   -.2578946
    numstate |   .0050961   .0020761     2.45   0.014     .0010224    .0091698
   fmidyears |  -.6435531   .0442756   -14.54   0.000    -.7303316   -.5567746
  fmidyears2 |   .0262601   .0022983    11.43   0.000     .0217555    .0307647
  fmidyears3 |   -.000296   .0000333    -8.89   0.000    -.0003613   -.0002308
       _cons |  -.7214137   1.380562    -0.52   0.601    -3.427279    1.984451
------------------------------------------------------------------------------
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)

. 
. 
. order specification specnumber n bdmCoefficient bdmSE bdmpvalues CIElCoefficient CIElSE CIElpvalues

. replace bdmpvalues=2*normal(-abs(bdmCoefficient/bdmSE))
(32 real changes made)

. replace CIElpvalues=2*normal(-abs(CIElCoefficient/CIElSE))
(32 real changes made)

. keep specification specnumber n bdmCoefficient bdmSE bdmpvalues CIElCoefficient CIElSE CIElpvalues

. drop if specnumber==.
(436509 observations deleted)

. 
. saveold "robustnessMbdm.dta", replace
(note: file robustnessMbdm.dta not found)
file robustnessMbdm.dta saved

. 
. 
. 
. 
. 
end of do-file

. *Produces: robustnessMbdm.dta
. 
. do "12-09-24_loop_mi_robustness_h10dm.do"

. *12-09-24_loop_mi_robustness_h10dm.do
. 
. 
. clear

. macro drop _all

. 
. global filetree /Users/Allan/Dropbox/!!Papers/Liberal Peace/12-02-21_ISQ_commentary/DOR_ISQ_2013_Replication/M_R
> ep

. 
. cd "$filetree"
/Users/Allan/Dropbox/!!Papers/Liberal Peace/12-02-21_ISQ_commentary/DOR_ISQ_2013_Replication/M_Rep

. use "midata"

. 
. mi xtset, clear

. 
. **check that logit runs with midyears*
. sort ccode1 ccode2 year

. global controls lncprt mjpw contigl lndist numstate

. 
. gen specification="NA"

. gen specnumber=.
(436541 missing values generated)

. gen n=.
(436541 missing values generated)

. gen h10dmCoefficient=.
(436541 missing values generated)

. gen h10dmSE=.
(436541 missing values generated)

. gen CIElCoefficient=.
(436541 missing values generated)

. gen CIElSE=.
(436541 missing values generated)

. gen h10dmpvalues=.
(436541 missing values generated)

. gen CIElpvalues=.
(436541 missing values generated)

. 
. gen bdm=.
(436541 missing values generated)

. replace bdm=1 if dml>6 & dml<11
(45417 real changes made)

. replace bdm=0 if dml<=6 & dml>=-10 
(391124 real changes made)

. 
. mi passive: generate bdmCIElc= bdm*CIElc
m=0:
(398484 missing values generated)
m=1:
m=2:
m=3:
m=4:
m=5:
m=6:
m=7:
m=8:
m=9:
m=10:
m=11:
m=12:
m=13:
m=14:
m=15:
m=16:
m=17:
m=18:
m=19:
m=20:

. 
. gen h10dm=.
(436541 missing values generated)

. replace h10dm=1 if dml>9 & dml<11
(13555 real changes made)

. replace h10dm=0 if dml<=9 & dml>=-10 
(422986 real changes made)

. 
. mi passive: generate h10dmCIElc= h10dm*CIElc
m=0:
(398484 missing values generated)
m=1:
m=2:
m=3:
m=4:
m=5:
m=6:
m=7:
m=8:
m=9:
m=10:
m=11:
m=12:
m=13:
m=14:
m=15:
m=16:
m=17:
m=18:
m=19:
m=20:

. 
. **Code just to see if things are running
. mi estimate, dots: logit fmidonsl lnlifedeerl h10dm $controls fmidyears*, cl(dyadid) 

Imputations (20):
  .........10.........20 done

Multiple-imputation estimates                     Imputations     =         20
Logistic regression                               Number of obs   =     409703
                                                  Average RVI     =     0.0451
                                                  Largest FMI     =     0.2018
DF adjustment:   Large sample                     DF:     min     =     482.13
                                                          avg     =   2.03e+08
                                                          max     =   1.46e+09
Model F test:       Equal FMI                     F(   9,111240.8)=      76.88
Within VCE type:       Robust                     Prob > F        =     0.0000

                            (Within VCE adjusted for 13273 clusters in dyadid)
------------------------------------------------------------------------------
    fmidonsl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
 lnlifedeerl |  -.4617679   .1401478    -3.29   0.001    -.7371438    -.186392
       h10dm |          0  (omitted)
      lncprt |   -.362782   .1135674    -3.19   0.001      -.58537    -.140194
        mjpw |   1.614248   .4816264     3.35   0.001     .6702766    2.558219
     contigl |   2.937496   2.830467     1.04   0.299    -2.610117    8.485108
      lndist |  -.2077458   .3592439    -0.58   0.563    -.9118509    .4963594
    numstate |   .0112865   .0037011     3.05   0.002     .0040305    .0185426
   fmidyears |  -.4572259   .0740476    -6.17   0.000    -.6023565   -.3120952
  fmidyears2 |   .0156794   .0042776     3.67   0.000     .0072954    .0240633
  fmidyears3 |  -.0001521   .0000654    -2.33   0.020    -.0002803   -.0000239
       _cons |  -6.239349   2.784963    -2.24   0.025    -11.69778   -.7809213
------------------------------------------------------------------------------

. logit midonsl _1_lnlifedeerl h10dm $controls     midyears*, cl(dyadid) 

Iteration 0:   log pseudolikelihood = -9683.1099  
Iteration 1:   log pseudolikelihood = -7594.7683  
Iteration 2:   log pseudolikelihood =  -6675.259  
Iteration 3:   log pseudolikelihood = -6316.5687  
Iteration 4:   log pseudolikelihood = -6309.3592  
Iteration 5:   log pseudolikelihood = -6309.3387  
Iteration 6:   log pseudolikelihood = -6309.3387  

Logistic regression                               Number of obs   =     423001
                                                  Wald chi2(10)   =    1916.80
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -6309.3387                 Pseudo R2       =     0.3484

                               (Std. Err. adjusted for 13538 clusters in dyadid)
--------------------------------------------------------------------------------
               |               Robust
       midonsl |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
_1_lnlifedeerl |  -.1360996   .0533184    -2.55   0.011    -.2406018   -.0315974
         h10dm |  -1.556684   .3544417    -4.39   0.000    -2.251376   -.8619906
        lncprt |  -.2298741   .0595658    -3.86   0.000    -.3466209   -.1131274
          mjpw |   1.981038   .2392466     8.28   0.000     1.512123    2.449952
       contigl |  -4.407868    .728257    -6.05   0.000    -5.835225   -2.980511
        lndist |  -.9730946   .0995065    -9.78   0.000    -1.168124   -.7780653
      numstate |   .0056879   .0013532     4.20   0.000     .0030357    .0083402
      midyears |  -.4121017   .0280473   -14.69   0.000    -.4670735   -.3571299
     midyears2 |   .0162893   .0015518    10.50   0.000     .0132478    .0193308
     midyears3 |  -.0001878   .0000236    -7.96   0.000    -.0002341   -.0001416
         _cons |   2.732555   .7428534     3.68   0.000     1.276589    4.188521
--------------------------------------------------------------------------------

. logit midonsl _2_lnlifedeerl h10dm $controls     midyears*, cl(dyadid) 

Iteration 0:   log pseudolikelihood = -9683.1099  
Iteration 1:   log pseudolikelihood = -7597.3664  
Iteration 2:   log pseudolikelihood =   -6681.23  
Iteration 3:   log pseudolikelihood =  -6322.822  
Iteration 4:   log pseudolikelihood = -6315.6652  
Iteration 5:   log pseudolikelihood = -6315.6449  
Iteration 6:   log pseudolikelihood = -6315.6449  

Logistic regression                               Number of obs   =     423001
                                                  Wald chi2(10)   =    1938.69
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -6315.6449                 Pseudo R2       =     0.3478

                               (Std. Err. adjusted for 13538 clusters in dyadid)
--------------------------------------------------------------------------------
               |               Robust
       midonsl |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
_2_lnlifedeerl |  -.0963555   .0495692    -1.94   0.052    -.1935093    .0007983
         h10dm |  -1.677538   .3469093    -4.84   0.000    -2.357468   -.9976084
        lncprt |  -.2283737   .0592575    -3.85   0.000    -.3445162   -.1122312
          mjpw |   1.967127   .2381713     8.26   0.000     1.500319    2.433934
       contigl |  -4.351771   .7292175    -5.97   0.000    -5.781011   -2.922531
        lndist |  -.9670099   .0996172    -9.71   0.000    -1.162256   -.7717639
      numstate |   .0050628   .0013045     3.88   0.000     .0025061    .0076195
      midyears |  -.4131381   .0281134   -14.70   0.000    -.4682394   -.3580367
     midyears2 |   .0163644   .0015555    10.52   0.000     .0133157     .019413
     midyears3 |  -.0001891   .0000237    -7.99   0.000    -.0002355   -.0001427
         _cons |   2.711368   .7430746     3.65   0.000     1.254968    4.167767
--------------------------------------------------------------------------------

. logit fmidonsl _3_lnlifedeerl h10dm $controls    fmidyears*, cl(dyadid) nolog
note: h10dm != 0 predicts failure perfectly
      h10dm dropped and 13032 obs not used


Logistic regression                               Number of obs   =     409703
                                                  Wald chi2(9)    =     697.89
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -782.38054                 Pseudo R2       =     0.4113

                               (Std. Err. adjusted for 13273 clusters in dyadid)
--------------------------------------------------------------------------------
               |               Robust
      fmidonsl |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
_3_lnlifedeerl |  -.5293774   .1297003    -4.08   0.000    -.7835854   -.2751694
         h10dm |          0  (omitted)
        lncprt |  -.3601179   .1151672    -3.13   0.002    -.5858413   -.1343944
          mjpw |   1.585665   .4853675     3.27   0.001     .6343617    2.536967
       contigl |   2.935911   2.822362     1.04   0.298    -2.595817    8.467639
        lndist |  -.2052583   .3584625    -0.57   0.567     -.907832    .4973154
      numstate |   .0122434   .0037064     3.30   0.001      .004979    .0195078
     fmidyears |  -.4565453   .0737476    -6.19   0.000    -.6010879   -.3120027
    fmidyears2 |   .0156696   .0042517     3.69   0.000     .0073365    .0240028
    fmidyears3 |  -.0001522    .000065    -2.34   0.019    -.0002795   -.0000249
         _cons |   -6.29552   2.795555    -2.25   0.024    -11.77471    -.816334
--------------------------------------------------------------------------------

. ****
. 
. sort ccode1 ccode2 year

. 
. local j=0

. 
. **M2 m.i. with 4 dependent variables x interaction = 8 models
. local j=`j'+1

. local spec`j' "M''"

. local covars`j' "midonsl CIEl  h10dm                                       $controls midyears*"

. 
. 
. local j=`j'+1

. local spec`j' "OM''"

. local covars`j' "midongl CIEl  h10dm                                       $controls midyears*"

. 
. 
. local j=`j'+1

. local spec`j' "FM''"

. local covars`j' "fmidonsl CIEl  h10dm                                      $controls fmidyears*"

. 
. 
. local j=`j'+1

. local spec`j' "FOM''"

. local covars`j' "fmidongl CIEl  h10dm                                      $controls fmidyears*"

. 
. local j=`j'+1

. local spec`j' "IM''"

. local covars`j' "midonsl CIElc  h10dm h10dmCIElc                                           $controls midyears*"

. 
. 
. local j=`j'+1

. local spec`j' "OIM''"

. local covars`j' "midongl CIElc  h10dm    h10dmCIElc                                $controls midyears*"

. 
. 
. local j=`j'+1

. local spec`j' "FIM''"

. local covars`j' "fmidonsl CIElc  h10dm           h10dmCIElc                        $controls fmidyears*"

. 
. 
. local j=`j'+1

. local spec`j' "FOIM''"

. local covars`j' "fmidongl CIElc  h10dm          h10dmCIElc                                 $controls fmidyears*"

. 
. 
. 
. **M2 m.i. with DemocracyHigh with 4 dependent variables x interaction = 8 models
. 
. 
. **M2 with 4 dependent variables x interaction = 8 models
. local j=`j'+1

. local spec`j' "DM''"

. local covars`j' "midonsl CIEl  h10dm  dmh                                          $controls midyears*"

. 
. 
. local j=`j'+1

. local spec`j' "ODM''"

. local covars`j' "midongl CIEl  h10dm  dmh                                          $controls midyears*"

. 
. 
. local j=`j'+1

. local spec`j' "FDM''"

. local covars`j' "fmidonsl CIEl  h10dm  dmh                                         $controls fmidyears*"

. 
. 
. local j=`j'+1

. local spec`j' "FODM''"

. local covars`j'  "fmidongl CIEl  h10dm  dmh                                        $controls fmidyears*"

. 
. 
. local j=`j'+1

. local spec`j'  "IDM''"

. local covars`j'  "midonsl CIElc  h10dm h10dmCIElc        dmh                               $controls midyears*"

. 
. 
. local j=`j'+1

. local spec`j'  "OIDM''"

. local covars`j'  "midongl CIElc  h10dm   h10dmCIElc      dmh                       $controls midyears*"

. 
. 
. local j=`j'+1

. local spec`j'  "FIDM''"

. local covars`j'  "fmidonsl CIElc  h10dm                  h10dmCIElc      dmh               $controls fmidyears*"

. 
. 
. local j=`j'+1

. local spec`j'  "FOIDM''"

. local covars`j' "fmidongl CIElc  h10dm          h10dmCIElc       dmh                       $controls fmidyears*"

. 
. 
. 
. 
. ****With other measures of life insurance expenditures
. 
. 
. **M2 m.i. with 4 dependent variables x interaction = 8 models
. local j=`j'+1

. local spec`j' "L''"

. local covars`j' "midonsl lnlifepenl  h10dm                                         $controls midyears*"

. 
. 
. local j=`j'+1

. local spec`j' "OL''"

. local covars`j' "midongl lnlifepenl  h10dm                                         $controls midyears*"

. 
. 
. local j=`j'+1

. local spec`j' "FL''"

. local covars`j' "fmidonsl lnlifepenl  h10dm                                        $controls fmidyears*"

. 
. 
. local j=`j'+1

. local spec`j' "FOL''"

. local covars`j' "fmidongl lnlifepenl  h10dm                                        $controls fmidyears*"

. 
. local j=`j'+1

. local spec`j' "IL''"

. local covars`j' "midonsl CIElc  h10dm h10dmCIElc                                           $controls midyears*"

. 
. 
. local j=`j'+1

. local spec`j' "OIL''"

. local covars`j' "midongl CIElc  h10dm    h10dmCIElc                                $controls midyears*"

. 
. 
. local j=`j'+1

. local spec`j' "FIL''"

. local covars`j' "fmidonsl CIElc  h10dm           h10dmCIElc                        $controls fmidyears*"

. 
. 
. local j=`j'+1

. local spec`j' "FOIL''"

. local covars`j' "fmidongl CIElc  h10dm          h10dmCIElc                                 $controls fmidyears*"

. 
. 
. 
. **M2 m.i. with DemocracyHigh with 4 dependent variables x interaction = 8 models
. 
. local j=`j'+1

. local spec`j' "DL''"

. local covars`j' "midonsl lnlifepenl  h10dm  dmh                                            $controls midyears*"

. 
. 
. local j=`j'+1

. local spec`j' "ODL''"

. local covars`j' "midongl lnlifepenl  h10dm  dmh                                            $controls midyears*"

. 
. 
. local j=`j'+1

. local spec`j' "FDL''"

. local covars`j' "fmidonsl lnlifepenl  h10dm  dmh                                           $controls fmidyears*"

. 
. 
. local j=`j'+1

. local spec`j' "FODL''"

. local covars`j'  "fmidongl lnlifepenl  h10dm  dmh                                          $controls fmidyears*"

. 
. 
. local j=`j'+1

. local spec`j'  "IDL''"

. local covars`j'  "midonsl CIElc  h10dm h10dmCIElc        dmh                               $controls midyears*"

. 
. 
. local j=`j'+1

. local spec`j'  "OIDL''"

. local covars`j'  "midongl CIElc  h10dm   h10dmCIElc      dmh                       $controls midyears*"

. 
. 
. local j=`j'+1

. local spec`j'  "FIDL''"

. local covars`j'  "fmidonsl CIElc  h10dm                  h10dmCIElc      dmh               $controls fmidyears*"

. 
. 
. local j=`j'+1

. local spec`j'  "FOIDL''"

. local covars`j' "fmidongl CIElc  h10dm          h10dmCIElc       dmh                       $controls fmidyears*"

. 
. 
. sort ccode1 ccode2 year

. 
. **Estimating Models, Saving Values
. forvalues k=1(1)`j' {
  2. replace specification="`spec`k''" if _n==`k'
  3. mi estimate, dots: logit `covars`k'', cl(dyadid) nolog
  4. mat A=e(b_mi)
  5. mat V=e(V_mi)
  6. replace n= e(N)   if _n==`k'
  7. replace specnumber=`k' if _n==`k'
  8. replace h10dmCoefficient=A[1,2] if _n==`k'
  9. replace h10dmSE=(V[2,2])^(1/2) if _n==`k'
 10. replace CIElCoefficient=A[1,1] if _n==`k'
 11. replace CIElSE=(V[1,1])^(1/2) if _n==`k'
 12. }
specification was str2 now str3
(1 real change made)

Imputations (20):
  .........10.........20 done

Multiple-imputation estimates                     Imputations     =         20
Logistic regression                               Number of obs   =     423001
                                                  Average RVI     =     0.0235
                                                  Largest FMI     =     0.1667
DF adjustment:   Large sample                     DF:     min     =     703.89
                                                          avg     =   5.52e+07
                                                          max     =   4.13e+08
Model F test:       Equal FMI                     F(  10,390093.9)=     189.30
Within VCE type:       Robust                     Prob > F        =     0.0000

                            (Within VCE adjusted for 13538 clusters in dyadid)
------------------------------------------------------------------------------
     midonsl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        CIEl |  -.1166948   .0555222    -2.10   0.036    -.2257037   -.0076858
       h10dm |  -1.611624   .3565684    -4.52   0.000    -2.310538   -.9127095
      lncprt |  -.2294917   .0594892    -3.86   0.000    -.3460885    -.112895
        mjpw |    1.97714   .2392928     8.26   0.000     1.508135    2.446145
     contigl |   -4.39141   .7316453    -6.00   0.000     -5.82541    -2.95741
      lndist |  -.9714925   .0998944    -9.73   0.000    -1.167282    -.775703
    numstate |   .0054197   .0013698     3.96   0.000     .0027344    .0081049
    midyears |  -.4126415   .0280705   -14.70   0.000    -.4676586   -.3576244
   midyears2 |   .0163353   .0015537    10.51   0.000       .01329    .0193806
   midyears3 |  -.0001887   .0000236    -7.98   0.000     -.000235   -.0001423
       _cons |    2.72051   .7448545     3.65   0.000     1.260621    4.180398
------------------------------------------------------------------------------
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
specification was str3 now str4
(1 real change made)

Imputations (20):
  .........10.........20 done

Multiple-imputation estimates                     Imputations     =         20
Logistic regression                               Number of obs   =     423726
                                                  Average RVI     =     0.0370
                                                  Largest FMI     =     0.2348
DF adjustment:   Large sample                     DF:     min     =     357.62
                                                          avg     =   4.85e+07
                                                          max     =   3.43e+08
Model F test:       Equal FMI                     F(  10,165221.6)=     190.45
Within VCE type:       Robust                     Prob > F        =     0.0000

                            (Within VCE adjusted for 13538 clusters in dyadid)
------------------------------------------------------------------------------
     midongl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        CIEl |  -.1400036    .054893    -2.55   0.011    -.2479572   -.0320501
       h10dm |  -1.933121   .3467205    -5.58   0.000    -2.612787   -1.253454
      lncprt |  -.2611655   .0504025    -5.18   0.000    -.3599526   -.1623784
        mjpw |   2.186569   .2180301    10.03   0.000     1.759237      2.6139
     contigl |  -3.546971    .705848    -5.03   0.000     -4.93041   -2.163532
      lndist |  -.8258615   .0934437    -8.84   0.000    -1.009008    -.642715
    numstate |   .0059124   .0012298     4.81   0.000     .0035009    .0083239
    midyears |  -.5839074   .0280075   -20.85   0.000    -.6388012   -.5290137
   midyears2 |   .0250141   .0015784    15.85   0.000     .0219204    .0281077
   midyears3 |  -.0003062   .0000246   -12.43   0.000    -.0003544   -.0002579
       _cons |   2.443042   .7100216     3.44   0.001     1.051424    3.834659
------------------------------------------------------------------------------
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)

Imputations (20):
  .........10.........20 done

Multiple-imputation estimates                     Imputations     =         20
Logistic regression                               Number of obs   =     409703
                                                  Average RVI     =     0.0451
                                                  Largest FMI     =     0.2018
DF adjustment:   Large sample                     DF:     min     =     482.13
                                                          avg     =   2.03e+08
                                                          max     =   1.46e+09
Model F test:       Equal FMI                     F(   9,111240.8)=      76.88
Within VCE type:       Robust                     Prob > F        =     0.0000

                            (Within VCE adjusted for 13273 clusters in dyadid)
------------------------------------------------------------------------------
    fmidonsl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        CIEl |  -.4617679   .1401478    -3.29   0.001    -.7371438    -.186392
       h10dm |          0  (omitted)
      lncprt |   -.362782   .1135674    -3.19   0.001      -.58537    -.140194
        mjpw |   1.614248   .4816264     3.35   0.001     .6702766    2.558219
     contigl |   2.937496   2.830467     1.04   0.299    -2.610117    8.485108
      lndist |  -.2077458   .3592439    -0.58   0.563    -.9118509    .4963594
    numstate |   .0112865   .0037011     3.05   0.002     .0040305    .0185426
   fmidyears |  -.4572259   .0740476    -6.17   0.000    -.6023565   -.3120952
  fmidyears2 |   .0156794   .0042776     3.67   0.000     .0072954    .0240633
  fmidyears3 |  -.0001521   .0000654    -2.33   0.020    -.0002803   -.0000239
       _cons |  -6.239349   2.784963    -2.24   0.025    -11.69778   -.7809213
------------------------------------------------------------------------------
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Imputations (20):
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Multiple-imputation estimates                     Imputations     =         20
Logistic regression                               Number of obs   =     410397
                                                  Average RVI     =     0.1012
                                                  Largest FMI     =     0.3503
DF adjustment:   Large sample                     DF:     min     =     162.13
                                                          avg     = 7876678.57
                                                          max     =   3.05e+07
Model F test:       Equal FMI                     F(   9,23060.2) =      69.45
Within VCE type:       Robust                     Prob > F        =     0.0000

                            (Within VCE adjusted for 13273 clusters in dyadid)
------------------------------------------------------------------------------
    fmidongl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        CIEl |   -.380299   .0894705    -4.25   0.000    -.5569767   -.2036213
       h10dm |          0  (omitted)
      lncprt |  -.3645762   .0739316    -4.93   0.000    -.5094795   -.2196729
        mjpw |   2.237874   .3497395     6.40   0.000     1.552398    2.923351
     contigl |  -1.291375   1.359584    -0.95   0.342    -3.956112    1.373362
      lndist |  -.5705542   .1733574    -3.29   0.001    -.9103286   -.2307798
    numstate |   .0046952   .0021192     2.22   0.027     .0005378    .0088525
   fmidyears |  -.6561172   .0444464   -14.76   0.000    -.7432305    -.569004
  fmidyears2 |   .0269403   .0022864    11.78   0.000      .022459    .0314216
  fmidyears3 |  -.0003053   .0000328    -9.30   0.000    -.0003696   -.0002409
       _cons |   .0996736   1.359517     0.07   0.942    -2.564932    2.764279
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Imputations (20):
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Multiple-imputation estimates                     Imputations     =         20
Logistic regression                               Number of obs   =     423001
                                                  Average RVI     =     0.0233
                                                  Largest FMI     =     0.1662
DF adjustment:   Large sample                     DF:     min     =     707.72
                                                          avg     =   5.02e+07
                                                          max     =   3.90e+08
Model F test:       Equal FMI                     F(  11,437149.9)=     172.57
Within VCE type:       Robust                     Prob > F        =     0.0000

                            (Within VCE adjusted for 13538 clusters in dyadid)
------------------------------------------------------------------------------
     midonsl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       CIElc |  -.1180845   .0562567    -2.10   0.036    -.2285344   -.0076345
       h10dm |  -1.806363   .6186196    -2.92   0.004    -3.018863   -.5938635
  h10dmCIElc |   .0789543   .2222694     0.36   0.722    -.3566982    .5146068
      lncprt |  -.2295542   .0595199    -3.86   0.000     -.346211   -.1128974
        mjpw |   1.976773   .2394774     8.25   0.000     1.507406     2.44614
     contigl |   -4.39674   .7318535    -6.01   0.000    -5.831148   -2.962333
      lndist |  -.9720491   .0999193    -9.73   0.000    -1.167887   -.7762108
    numstate |   .0054091   .0013696     3.95   0.000     .0027242    .0080941
    midyears |  -.4127228   .0280665   -14.71   0.000    -.4677322   -.3577134
   midyears2 |   .0163411   .0015532    10.52   0.000     .0132968    .0193854
   midyears3 |  -.0001888   .0000236    -7.99   0.000    -.0002351   -.0001424
       _cons |   2.475402   .7478849     3.31   0.001     1.009575     3.94123
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Imputations (20):
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Multiple-imputation estimates                     Imputations     =         20
Logistic regression                               Number of obs   =     423726
                                                  Average RVI     =     0.0361
                                                  Largest FMI     =     0.2356
DF adjustment:   Large sample                     DF:     min     =     354.98
                                                          avg     =   4.45e+07
                                                          max     =   3.31e+08
Model F test:       Equal FMI                     F(  11,190950.3)=     173.69
Within VCE type:       Robust                     Prob > F        =     0.0000

                            (Within VCE adjusted for 13538 clusters in dyadid)
------------------------------------------------------------------------------
     midongl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       CIElc |  -.1418023   .0555126    -2.55   0.011    -.2509772   -.0326273
       h10dm |  -2.257178   .6150771    -3.67   0.000    -3.462734   -1.051622
  h10dmCIElc |   .1338194   .2145905     0.62   0.533     -.286797    .5544358
      lncprt |  -.2612162   .0504314    -5.18   0.000    -.3600598   -.1623725
        mjpw |   2.186002   .2181638    10.02   0.000     1.758409    2.613596
     contigl |   -3.55422   .7061992    -5.03   0.000    -4.938348   -2.170093
      lndist |  -.8266181   .0934803    -8.84   0.000    -1.009836      -.6434
    numstate |   .0059028     .00123     4.80   0.000     .0034907    .0083148
    midyears |   -.584009   .0280019   -20.86   0.000    -.6388919   -.5291262
   midyears2 |   .0250212   .0015778    15.86   0.000     .0219288    .0281137
   midyears3 |  -.0003063   .0000246   -12.45   0.000    -.0003545   -.0002581
       _cons |   2.149004   .7116047     3.02   0.003     .7542844    3.543724
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Imputations (20):
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Multiple-imputation estimates                     Imputations     =         20
Logistic regression                               Number of obs   =     409703
                                                  Average RVI     =     0.0451
                                                  Largest FMI     =     0.2018
DF adjustment:   Large sample                     DF:     min     =     482.13
                                                          avg     =   2.02e+08
                                                          max     =   1.46e+09
Model F test:       Equal FMI                     F(   9,111240.8)=      76.88
Within VCE type:       Robust                     Prob > F        =     0.0000

                            (Within VCE adjusted for 13273 clusters in dyadid)
------------------------------------------------------------------------------
    fmidonsl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       CIElc |  -.4617679   .1401478    -3.29   0.001    -.7371438    -.186392
       h10dm |          0  (omitted)
  h10dmCIElc |          0  (omitted)
      lncprt |   -.362782   .1135674    -3.19   0.001      -.58537    -.140194
        mjpw |   1.614248   .4816264     3.35   0.001     .6702766    2.558219
     contigl |   2.937496   2.830467     1.04   0.299    -2.610117    8.485108
      lndist |  -.2077458   .3592439    -0.58   0.563    -.9118509    .4963594
    numstate |   .0112865   .0037011     3.05   0.002     .0040305    .0185426
   fmidyears |  -.4572259   .0740476    -6.17   0.000    -.6023565   -.3120952
  fmidyears2 |   .0156794   .0042776     3.67   0.000     .0072954    .0240633
  fmidyears3 |  -.0001521   .0000654    -2.33   0.020    -.0002803   -.0000239
       _cons |  -7.231257   2.811764    -2.57   0.010    -12.74222   -1.720292
------------------------------------------------------------------------------
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Imputations (20):
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Multiple-imputation estimates                     Imputations     =         20
Logistic regression                               Number of obs   =     410397
                                                  Average RVI     =     0.1012
                                                  Largest FMI     =     0.3503
DF adjustment:   Large sample                     DF:     min     =     162.13
                                                          avg     = 7383412.45
                                                          max     =   3.05e+07
Model F test:       Equal FMI                     F(   9,23060.2) =      69.45
Within VCE type:       Robust                     Prob > F        =     0.0000

                            (Within VCE adjusted for 13273 clusters in dyadid)
------------------------------------------------------------------------------
    fmidongl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       CIElc |   -.380299   .0894705    -4.25   0.000    -.5569767   -.2036213
       h10dm |          0  (omitted)
  h10dmCIElc |          0  (omitted)
      lncprt |  -.3645762   .0739316    -4.93   0.000    -.5094795   -.2196729
        mjpw |   2.237874   .3497395     6.40   0.000     1.552398    2.923351
     contigl |  -1.291375   1.359584    -0.95   0.342    -3.956112    1.373362
      lndist |  -.5705542   .1733574    -3.29   0.001    -.9103286   -.2307798
    numstate |   .0046952   .0021192     2.22   0.027     .0005378    .0088525
   fmidyears |  -.6561172   .0444464   -14.76   0.000    -.7432305    -.569004
  fmidyears2 |   .0269403   .0022864    11.78   0.000      .022459    .0314216
  fmidyears3 |  -.0003053   .0000328    -9.30   0.000    -.0003696   -.0002409
       _cons |  -.7172337   1.366435    -0.52   0.600    -3.395406    1.960939
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Imputations (20):
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Multiple-imputation estimates                     Imputations     =         20
Logistic regression                               Number of obs   =     423001
                                                  Average RVI     =     0.0262
                                                  Largest FMI     =     0.1670
DF adjustment:   Large sample                     DF:     min     =     701.03
                                                          avg     =   4.17e+07
                                                          max     =   4.35e+08
Model F test:       Equal FMI                     F(  11,464590.8)=     177.39
Within VCE type:       Robust                     Prob > F        =     0.0000

                            (Within VCE adjusted for 13538 clusters in dyadid)
------------------------------------------------------------------------------
     midonsl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        CIEl |  -.1739246   .0578322    -3.01   0.003    -.2874697   -.0603795
       h10dm |  -1.702383   .3568703    -4.77   0.000    -2.401877   -1.002889
         dmh |    .040861   .0097211     4.20   0.000     .0218079     .059914
      lncprt |  -.2254347   .0566749    -3.98   0.000    -.3365154   -.1143539
        mjpw |   1.921027   .2257249     8.51   0.000     1.478614     2.36344
     contigl |  -4.430174   .7237483    -6.12   0.000    -5.848696   -3.011653
      lndist |  -.9934372   .0978384   -10.15   0.000    -1.185197   -.8016773
    numstate |    .005367   .0013492     3.98   0.000      .002722     .008012
    midyears |  -.3984832   .0276554   -14.41   0.000    -.4526867   -.3442797
   midyears2 |     .01576   .0015377    10.25   0.000     .0127461     .018774
   midyears3 |  -.0001836   .0000235    -7.80   0.000    -.0002298   -.0001375
       _cons |    2.75943   .7338334     3.76   0.000     1.321142    4.197717
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Imputations (20):
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Multiple-imputation estimates                     Imputations     =         20
Logistic regression                               Number of obs   =     423726
                                                  Average RVI     =     0.0410
                                                  Largest FMI     =     0.2469
DF adjustment:   Large sample                     DF:     min     =     323.68
                                                          avg     =   3.26e+07
                                                          max     =   3.32e+08
Model F test:       Equal FMI                     F(  11,176450.7)=     180.41
Within VCE type:       Robust                     Prob > F        =     0.0000

                            (Within VCE adjusted for 13538 clusters in dyadid)
------------------------------------------------------------------------------
     midongl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        CIEl |  -.1836604   .0564332    -3.25   0.001    -.2946826   -.0726382
       h10dm |  -2.004899   .3479197    -5.76   0.000    -2.686898   -1.322901
         dmh |   .0325008   .0092221     3.52   0.000     .0144254    .0505762
      lncprt |  -.2572264   .0483295    -5.32   0.000    -.3519505   -.1625022
        mjpw |   2.123478   .2078659    10.22   0.000     1.716068    2.530887
     contigl |  -3.599281   .6971187    -5.16   0.000    -4.965611   -2.232951
      lndist |  -.8457982   .0912115    -9.27   0.000     -1.02457   -.6670267
    numstate |   .0058275    .001208     4.82   0.000     .0034584    .0081965
    midyears |  -.5728718   .0278395   -20.58   0.000    -.6274361   -.5183074
   midyears2 |   .0245712   .0015725    15.63   0.000      .021489    .0276533
   midyears3 |  -.0003024   .0000246   -12.28   0.000    -.0003507   -.0002542
       _cons |    2.50642   .7007451     3.58   0.000     1.132984    3.879856
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Imputations (20):
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Multiple-imputation estimates                     Imputations     =         20
Logistic regression                               Number of obs   =     409703
                                                  Average RVI     =     0.0439
                                                  Largest FMI     =     0.1840
DF adjustment:   Large sample                     DF:     min     =     578.77
                                                          avg     =   5.11e+07
                                                          max     =   3.01e+08
Model F test:       Equal FMI                     F(  10,138955.7)=      64.66
Within VCE type:       Robust                     Prob > F        =     0.0000

                            (Within VCE adjusted for 13273 clusters in dyadid)
------------------------------------------------------------------------------
    fmidonsl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        CIEl |  -.5048975   .1387211    -3.64   0.000    -.7773556   -.2324395
       h10dm |          0  (omitted)
         dmh |   .0421948   .0264683     1.59   0.111    -.0096822    .0940719
      lncprt |  -.3582804   .1089066    -3.29   0.001    -.5717336   -.1448273
        mjpw |   1.637223   .4688635     3.49   0.000      .718266     2.55618
     contigl |   2.814243   2.763883     1.02   0.309    -2.602867    8.231354
      lndist |  -.2374112   .3490486    -0.68   0.496    -.9215338    .4467114
    numstate |   .0114327   .0036877     3.10   0.002     .0042025    .0186629
   fmidyears |  -.4454535   .0751847    -5.92   0.000    -.5928127   -.2980943
  fmidyears2 |   .0152993   .0043395     3.53   0.000      .006794    .0238046
  fmidyears3 |  -.0001509   .0000665    -2.27   0.023    -.0002811   -.0000206
       _cons |  -6.186773   2.726163    -2.27   0.023    -11.52996   -.8435899
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Imputations (20):
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Multiple-imputation estimates                     Imputations     =         20
Logistic regression                               Number of obs   =     410397
                                                  Average RVI     =     0.1043
                                                  Largest FMI     =     0.3658
DF adjustment:   Large sample                     DF:     min     =     148.78
                                                          avg     = 4210679.63
                                                          max     =   1.31e+07
Model F test:       Equal FMI                     F(  10,25733.1) =      66.02
Within VCE type:       Robust                     Prob > F        =     0.0000

                            (Within VCE adjusted for 13273 clusters in dyadid)
------------------------------------------------------------------------------
    fmidongl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        CIEl |  -.4202107   .0881976    -4.76   0.000    -.5944924   -.2459289
       h10dm |          0  (omitted)
         dmh |   .0359268   .0153056     2.35   0.019     .0059279    .0659256
      lncprt |  -.3576419   .0709747    -5.04   0.000    -.4967498    -.218534
        mjpw |   2.191131   .3281274     6.68   0.000     1.548013    2.834249
     contigl |  -1.351388   1.338011    -1.01   0.312    -3.973843    1.271066
      lndist |   -.592172   .1673924    -3.54   0.000    -.9202552   -.2640887
    numstate |     .00475   .0020602     2.31   0.021     .0007077    .0087923
   fmidyears |  -.6453015   .0442592   -14.58   0.000    -.7320479    -.558555
  fmidyears2 |   .0265386   .0022844    11.62   0.000     .0220612     .031016
  fmidyears3 |  -.0003027   .0000329    -9.19   0.000    -.0003672   -.0002381
       _cons |   .1296143   1.344039     0.10   0.923    -2.504655    2.763883
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Imputations (20):
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Multiple-imputation estimates                     Imputations     =         20
Logistic regression                               Number of obs   =     423001
                                                  Average RVI     =     0.0259
                                                  Largest FMI     =     0.1667
DF adjustment:   Large sample                     DF:     min     =     703.46
                                                          avg     =   3.63e+07
                                                          max     =   4.04e+08
Model F test:       Equal FMI                     F(  12,509333.4)=     163.28
Within VCE type:       Robust                     Prob > F        =     0.0000

                            (Within VCE adjusted for 13538 clusters in dyadid)
------------------------------------------------------------------------------
     midonsl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       CIElc |  -.1765308    .058691    -3.01   0.003    -.2917613   -.0613003
       h10dm |  -2.033429   .6229951    -3.26   0.001    -3.254502   -.8123566
  h10dmCIElc |   .1369379   .2236794     0.61   0.540    -.3014781     .575354
         dmh |    .041007   .0097278     4.22   0.000     .0219406    .0600733
      lncprt |  -.2255375   .0567074    -3.98   0.000    -.3366819   -.1143931
        mjpw |   1.920228   .2259435     8.50   0.000     1.477386    2.363069
     contigl |  -4.439272    .723675    -6.13   0.000     -5.85765   -3.020893
      lndist |  -.9944495   .0978376   -10.16   0.000    -1.186208   -.8026912
    numstate |   .0053506   .0013491     3.97   0.000     .0027058    .0079955
    midyears |  -.3985603   .0276514   -14.41   0.000    -.4527561   -.3443645
   midyears2 |   .0157667   .0015372    10.26   0.000     .0127539    .0187795
   midyears3 |  -.0001838   .0000235    -7.81   0.000    -.0002299   -.0001376
       _cons |   2.394508   .7400697     3.24   0.001     .9439977    3.845019
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Imputations (20):
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Multiple-imputation estimates                     Imputations     =         20
Logistic regression                               Number of obs   =     423726
                                                  Average RVI     =     0.0401
                                                  Largest FMI     =     0.2481
DF adjustment:   Large sample                     DF:     min     =     320.74
                                                          avg     =   2.97e+07
                                                          max     =   3.23e+08
Model F test:       Equal FMI                     F(  12,200718.9)=     166.04
Within VCE type:       Robust                     Prob > F        =     0.0000

                            (Within VCE adjusted for 13538 clusters in dyadid)
------------------------------------------------------------------------------
     midongl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       CIElc |  -.1862421   .0571265    -3.26   0.001    -.2986322   -.0738521
       h10dm |  -2.430913   .6172653    -3.94   0.000    -3.640756    -1.22107
  h10dmCIElc |   .1786802   .2153599     0.83   0.407    -.2434463    .6008067
         dmh |    .032636   .0092243     3.54   0.000     .0145564    .0507156
      lncprt |  -.2572854    .048352    -5.32   0.000    -.3520535   -.1625172
        mjpw |   2.122464   .2079872    10.20   0.000     1.714817    2.530112
     contigl |  -3.609055   .6973253    -5.18   0.000     -4.97579    -2.24232
      lndist |   -.846879   .0912384    -9.28   0.000    -1.025703   -.6680547
    numstate |   .0058156   .0012084     4.81   0.000     .0034457    .0081855
    midyears |  -.5729556   .0278367   -20.58   0.000    -.6275145   -.5183966
   midyears2 |   .0245781   .0015721    15.63   0.000     .0214969    .0276593
   midyears3 |  -.0003025   .0000246   -12.30   0.000    -.0003508   -.0002543
       _cons |   2.120365   .7046833     3.01   0.003       .73921     3.50152
------------------------------------------------------------------------------
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Imputations (20):
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Multiple-imputation estimates                     Imputations     =         20
Logistic regression                               Number of obs   =     409703
                                                  Average RVI     =     0.0439
                                                  Largest FMI     =     0.1840
DF adjustment:   Large sample                     DF:     min     =     578.77
                                                          avg     =   5.04e+07
                                                          max     =   3.01e+08
Model F test:       Equal FMI                     F(  10,138955.7)=      64.66
Within VCE type:       Robust                     Prob > F        =     0.0000

                            (Within VCE adjusted for 13273 clusters in dyadid)
------------------------------------------------------------------------------
    fmidonsl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       CIElc |  -.5048975   .1387211    -3.64   0.000    -.7773556   -.2324395
       h10dm |          0  (omitted)
  h10dmCIElc |          0  (omitted)
         dmh |   .0421948   .0264683     1.59   0.111    -.0096822    .0940719
      lncprt |  -.3582804   .1089066    -3.29   0.001    -.5717336   -.1448273
        mjpw |   1.637223   .4688635     3.49   0.000      .718266     2.55618
     contigl |   2.814243   2.763883     1.02   0.309    -2.602867    8.231354
      lndist |  -.2374112   .3490486    -0.68   0.496    -.9215338    .4467114
    numstate |   .0114327   .0036877     3.10   0.002     .0042025    .0186629
   fmidyears |  -.4454535   .0751847    -5.92   0.000    -.5928127   -.2980943
  fmidyears2 |   .0152993   .0043395     3.53   0.000      .006794    .0238046
  fmidyears3 |  -.0001509   .0000665    -2.27   0.023    -.0002811   -.0000206
       _cons |  -7.271326   2.763379    -2.63   0.009    -12.68746   -1.855193
------------------------------------------------------------------------------
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Imputations (20):
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Multiple-imputation estimates                     Imputations     =         20
Logistic regression                               Number of obs   =     410397
                                                  Average RVI     =     0.1043
                                                  Largest FMI     =     0.3658
DF adjustment:   Large sample                     DF:     min     =     148.78
                                                          avg     = 3950686.88
                                                          max     =   1.31e+07
Model F test:       Equal FMI                     F(  10,25733.1) =      66.02
Within VCE type:       Robust                     Prob > F        =     0.0000

                            (Within VCE adjusted for 13273 clusters in dyadid)
------------------------------------------------------------------------------
    fmidongl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       CIElc |  -.4202107   .0881976    -4.76   0.000    -.5944924   -.2459289
       h10dm |          0  (omitted)
  h10dmCIElc |          0  (omitted)
         dmh |   .0359268   .0153056     2.35   0.019     .0059279    .0659256
      lncprt |  -.3576419   .0709747    -5.04   0.000    -.4967498    -.218534
        mjpw |   2.191131   .3281274     6.68   0.000     1.548013    2.834249
     contigl |  -1.351388   1.338011    -1.01   0.312    -3.973843    1.271066
      lndist |   -.592172   .1673924    -3.54   0.000    -.9202552   -.2640887
    numstate |     .00475   .0020602     2.31   0.021     .0007077    .0087923
   fmidyears |  -.6453015   .0442592   -14.58   0.000    -.7320479    -.558555
  fmidyears2 |   .0265386   .0022844    11.62   0.000     .0220612     .031016
  fmidyears3 |  -.0003027   .0000329    -9.19   0.000    -.0003672   -.0002381
       _cons |   -.773026    1.36239    -0.57   0.570    -3.443271    1.897219
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Imputations (20):
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Multiple-imputation estimates                     Imputations     =         20
Logistic regression                               Number of obs   =     423001
                                                  Average RVI     =     0.0201
                                                  Largest FMI     =     0.0806
DF adjustment:   Large sample                     DF:     min     =    2969.80
                                                          avg     =   4.17e+07
                                                          max     =   2.71e+08
Model F test:       Equal FMI                     F(  10, 1.0e+06)=     192.37
Within VCE type:       Robust                     Prob > F        =     0.0000

                            (Within VCE adjusted for 13538 clusters in dyadid)
------------------------------------------------------------------------------
     midonsl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  lnlifepenl |  -.2216672   .0515547    -4.30   0.000    -.3227537   -.1205806
       h10dm |  -1.591188   .3368833    -4.72   0.000    -2.251469   -.9309063
      lncprt |  -.2246981   .0590607    -3.80   0.000     -.340455   -.1089412
        mjpw |   2.000435   .2380673     8.40   0.000     1.533832    2.467039
     contigl |  -4.316852   .7285936    -5.92   0.000    -5.744869   -2.888834
      lndist |  -.9581043   .0994345    -9.64   0.000    -1.152992   -.7632162
    numstate |   .0057957   .0013243     4.38   0.000        .0032    .0083914
    midyears |  -.4106285   .0280298   -14.65   0.000     -.465566   -.3556911
   midyears2 |   .0161532   .0015479    10.44   0.000     .0131194     .019187
   midyears3 |  -.0001855   .0000235    -7.90   0.000    -.0002315   -.0001394
       _cons |   .9794461   .9009578     1.09   0.277    -.7864168    2.745309
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Imputations (20):
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Multiple-imputation estimates                     Imputations     =         20
Logistic regression                               Number of obs   =     423726
                                                  Average RVI     =     0.0300
                                                  Largest FMI     =     0.1576
DF adjustment:   Large sample                     DF:     min     =     785.82
                                                          avg     =   1.82e+07
                                                          max     =   7.46e+07
Model F test:       Equal FMI                     F(  10,344535.3)=     192.51
Within VCE type:       Robust                     Prob > F        =     0.0000

                            (Within VCE adjusted for 13538 clusters in dyadid)
------------------------------------------------------------------------------
     midongl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  lnlifepenl |   -.212964   .0481958    -4.42   0.000    -.3075716   -.1183563
       h10dm |  -1.995954   .3294027    -6.06   0.000     -2.64158   -1.350329
      lncprt |  -.2546576   .0494313    -5.15   0.000    -.3515412    -.157774
        mjpw |   2.187239   .2119313    10.32   0.000     1.771862    2.602617
     contigl |  -3.419887   .7023153    -4.87   0.000      -4.7964   -2.043375
      lndist |  -.8062676   .0930639    -8.66   0.000    -.9886694   -.6238658
    numstate |   .0058678   .0011616     5.05   0.000     .0035909    .0081446
    midyears |  -.5826906   .0280058   -20.81   0.000     -.637581   -.5278002
   midyears2 |   .0248828    .001574    15.81   0.000     .0217978    .0279679
   midyears3 |  -.0003037   .0000245   -12.41   0.000    -.0003517   -.0002558
       _cons |   .7444565    .832741     0.89   0.371    -.8877608    2.376674
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Imputations (20):
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Multiple-imputation estimates                     Imputations     =         20
Logistic regression                               Number of obs   =     409703
                                                  Average RVI     =     0.0165
                                                  Largest FMI     =     0.0958
DF adjustment:   Large sample                     DF:     min     =    2106.93
                                                          avg     =   9.41e+08
                                                          max     =   6.80e+09
Model F test:       Equal FMI                     F(   9,631226.3)=     107.93
Within VCE type:       Robust                     Prob > F        =     0.0000

                            (Within VCE adjusted for 13273 clusters in dyadid)
------------------------------------------------------------------------------
    fmidonsl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  lnlifepenl |  -.2845491   .1016389    -2.80   0.005    -.4838721    -.085226
       h10dm |          0  (omitted)
      lncprt |  -.3657082   .1043059    -3.51   0.000    -.5701441   -.1612723
        mjpw |   1.663618   .4605693     3.61   0.000     .7609185    2.566318
     contigl |   3.312635    2.71506     1.22   0.222    -2.008785    8.634055
      lndist |  -.1604472   .3463732    -0.46   0.643    -.8393261    .5184318
    numstate |    .007756   .0029183     2.66   0.008     .0020361    .0134759
   fmidyears |  -.4591064   .0742868    -6.18   0.000    -.6047058   -.3135071
  fmidyears2 |   .0158791    .004285     3.71   0.000     .0074807    .0242775
  fmidyears3 |  -.0001565   .0000654    -2.39   0.017    -.0002847   -.0000284
       _cons |  -8.461703    2.85729    -2.96   0.003    -14.06192   -2.861481
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Imputations (20):
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Multiple-imputation estimates                     Imputations     =         20
Logistic regression                               Number of obs   =     410397
                                                  Average RVI     =     0.0713
                                                  Largest FMI     =     0.3232
DF adjustment:   Large sample                     DF:     min     =     190.11
                                                          avg     =   7.17e+07
                                                          max     =   3.01e+08
Model F test:       Equal FMI                     F(   9,35491.2) =      89.67
Within VCE type:       Robust                     Prob > F        =     0.0000

                            (Within VCE adjusted for 13273 clusters in dyadid)
------------------------------------------------------------------------------
    fmidongl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  lnlifepenl |  -.2745931   .0615807    -4.46   0.000    -.3960623   -.1531238
       h10dm |          0  (omitted)
      lncprt |  -.3531473    .068761    -5.14   0.000    -.4879164   -.2183781
        mjpw |   2.199342    .334817     6.57   0.000     1.543112    2.855571
     contigl |  -.8563288    1.34623    -0.64   0.525     -3.49489    1.782233
      lndist |  -.5139473   .1728393    -2.97   0.003    -.8527062   -.1751885
    numstate |   .0022854   .0017257     1.32   0.185    -.0010974    .0056682
   fmidyears |  -.6556379   .0446212   -14.69   0.000    -.7430939    -.568182
  fmidyears2 |   .0269466   .0022934    11.75   0.000     .0224516    .0314416
  fmidyears3 |  -.0003064   .0000329    -9.32   0.000    -.0003708    -.000242
       _cons |  -2.222558   1.507262    -1.47   0.140    -5.176963    .7318472
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Imputations (20):
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Multiple-imputation estimates                     Imputations     =         20
Logistic regression                               Number of obs   =     423001
                                                  Average RVI     =     0.0233
                                                  Largest FMI     =     0.1662
DF adjustment:   Large sample                     DF:     min     =     707.72
                                                          avg     =   5.02e+07
                                                          max     =   3.90e+08
Model F test:       Equal FMI                     F(  11,437149.9)=     172.57
Within VCE type:       Robust                     Prob > F        =     0.0000

                            (Within VCE adjusted for 13538 clusters in dyadid)
------------------------------------------------------------------------------
     midonsl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       CIElc |  -.1180845   .0562567    -2.10   0.036    -.2285344   -.0076345
       h10dm |  -1.806363   .6186196    -2.92   0.004    -3.018863   -.5938635
  h10dmCIElc |   .0789543   .2222694     0.36   0.722    -.3566982    .5146068
      lncprt |  -.2295542   .0595199    -3.86   0.000     -.346211   -.1128974
        mjpw |   1.976773   .2394774     8.25   0.000     1.507406     2.44614
     contigl |   -4.39674   .7318535    -6.01   0.000    -5.831148   -2.962333
      lndist |  -.9720491   .0999193    -9.73   0.000    -1.167887   -.7762108
    numstate |   .0054091   .0013696     3.95   0.000     .0027242    .0080941
    midyears |  -.4127228   .0280665   -14.71   0.000    -.4677322   -.3577134
   midyears2 |   .0163411   .0015532    10.52   0.000     .0132968    .0193854
   midyears3 |  -.0001888   .0000236    -7.99   0.000    -.0002351   -.0001424
       _cons |   2.475402   .7478849     3.31   0.001     1.009575     3.94123
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Imputations (20):
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Multiple-imputation estimates                     Imputations     =         20
Logistic regression                               Number of obs   =     423726
                                                  Average RVI     =     0.0361
                                                  Largest FMI     =     0.2356
DF adjustment:   Large sample                     DF:     min     =     354.98
                                                          avg     =   4.45e+07
                                                          max     =   3.31e+08
Model F test:       Equal FMI                     F(  11,190950.3)=     173.69
Within VCE type:       Robust                     Prob > F        =     0.0000

                            (Within VCE adjusted for 13538 clusters in dyadid)
------------------------------------------------------------------------------
     midongl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       CIElc |  -.1418023   .0555126    -2.55   0.011    -.2509772   -.0326273
       h10dm |  -2.257178   .6150771    -3.67   0.000    -3.462734   -1.051622
  h10dmCIElc |   .1338194   .2145905     0.62   0.533     -.286797    .5544358
      lncprt |  -.2612162   .0504314    -5.18   0.000    -.3600598   -.1623725
        mjpw |   2.186002   .2181638    10.02   0.000     1.758409    2.613596
     contigl |   -3.55422   .7061992    -5.03   0.000    -4.938348   -2.170093
      lndist |  -.8266181   .0934803    -8.84   0.000    -1.009836      -.6434
    numstate |   .0059028     .00123     4.80   0.000     .0034907    .0083148
    midyears |   -.584009   .0280019   -20.86   0.000    -.6388919   -.5291262
   midyears2 |   .0250212   .0015778    15.86   0.000     .0219288    .0281137
   midyears3 |  -.0003063   .0000246   -12.45   0.000    -.0003545   -.0002581
       _cons |   2.149004   .7116047     3.02   0.003     .7542844    3.543724
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Imputations (20):
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Multiple-imputation estimates                     Imputations     =         20
Logistic regression                               Number of obs   =     409703
                                                  Average RVI     =     0.0451
                                                  Largest FMI     =     0.2018
DF adjustment:   Large sample                     DF:     min     =     482.13
                                                          avg     =   2.02e+08
                                                          max     =   1.46e+09
Model F test:       Equal FMI                     F(   9,111240.8)=      76.88
Within VCE type:       Robust                     Prob > F        =     0.0000

                            (Within VCE adjusted for 13273 clusters in dyadid)
------------------------------------------------------------------------------
    fmidonsl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       CIElc |  -.4617679   .1401478    -3.29   0.001    -.7371438    -.186392
       h10dm |          0  (omitted)
  h10dmCIElc |          0  (omitted)
      lncprt |   -.362782   .1135674    -3.19   0.001      -.58537    -.140194
        mjpw |   1.614248   .4816264     3.35   0.001     .6702766    2.558219
     contigl |   2.937496   2.830467     1.04   0.299    -2.610117    8.485108
      lndist |  -.2077458   .3592439    -0.58   0.563    -.9118509    .4963594
    numstate |   .0112865   .0037011     3.05   0.002     .0040305    .0185426
   fmidyears |  -.4572259   .0740476    -6.17   0.000    -.6023565   -.3120952
  fmidyears2 |   .0156794   .0042776     3.67   0.000     .0072954    .0240633
  fmidyears3 |  -.0001521   .0000654    -2.33   0.020    -.0002803   -.0000239
       _cons |  -7.231257   2.811764    -2.57   0.010    -12.74222   -1.720292
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Imputations (20):
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Multiple-imputation estimates                     Imputations     =         20
Logistic regression                               Number of obs   =     410397
                                                  Average RVI     =     0.1012
                                                  Largest FMI     =     0.3503
DF adjustment:   Large sample                     DF:     min     =     162.13
                                                          avg     = 7383412.45
                                                          max     =   3.05e+07
Model F test:       Equal FMI                     F(   9,23060.2) =      69.45
Within VCE type:       Robust                     Prob > F        =     0.0000

                            (Within VCE adjusted for 13273 clusters in dyadid)
------------------------------------------------------------------------------
    fmidongl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       CIElc |   -.380299   .0894705    -4.25   0.000    -.5569767   -.2036213
       h10dm |          0  (omitted)
  h10dmCIElc |          0  (omitted)
      lncprt |  -.3645762   .0739316    -4.93   0.000    -.5094795   -.2196729
        mjpw |   2.237874   .3497395     6.40   0.000     1.552398    2.923351
     contigl |  -1.291375   1.359584    -0.95   0.342    -3.956112    1.373362
      lndist |  -.5705542   .1733574    -3.29   0.001    -.9103286   -.2307798
    numstate |   .0046952   .0021192     2.22   0.027     .0005378    .0088525
   fmidyears |  -.6561172   .0444464   -14.76   0.000    -.7432305    -.569004
  fmidyears2 |   .0269403   .0022864    11.78   0.000      .022459    .0314216
  fmidyears3 |  -.0003053   .0000328    -9.30   0.000    -.0003696   -.0002409
       _cons |  -.7172337   1.366435    -0.52   0.600    -3.395406    1.960939
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Imputations (20):
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Multiple-imputation estimates                     Imputations     =         20
Logistic regression                               Number of obs   =     423001
                                                  Average RVI     =     0.0251
                                                  Largest FMI     =     0.1100
DF adjustment:   Large sample                     DF:     min     =    1603.09
                                                          avg     =   1.48e+07
                                                          max     =   1.01e+08
Model F test:       Equal FMI                     F(  11,659527.4)=     187.35
Within VCE type:       Robust                     Prob > F        =     0.0000

                            (Within VCE adjusted for 13538 clusters in dyadid)
------------------------------------------------------------------------------
     midonsl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  lnlifepenl |  -.2557492   .0467178    -5.47   0.000    -.3473835   -.1641148
       h10dm |  -1.784084   .3376392    -5.28   0.000    -2.445847   -1.122321
         dmh |   .0398238   .0091439     4.36   0.000     .0219021    .0577455
      lncprt |    -.21712   .0559964    -3.88   0.000     -.326871   -.1073689
        mjpw |   1.920676   .2225007     8.63   0.000     1.484583     2.35677
     contigl |  -4.264547   .7210313    -5.91   0.000    -5.677743   -2.851352
      lndist |  -.9680873   .0973134    -9.95   0.000    -1.158818   -.7773564
    numstate |    .005322   .0013038     4.08   0.000     .0027666    .0078774
    midyears |  -.3977225   .0277281   -14.34   0.000    -.4520686   -.3433764
   midyears2 |   .0156404   .0015349    10.19   0.000     .0126321    .0186486
   midyears3 |  -.0001813   .0000234    -7.75   0.000    -.0002271   -.0001354
       _cons |   .6988459    .877166     0.80   0.426    -1.020394    2.418086
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Imputations (20):
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Multiple-imputation estimates                     Imputations     =         20
Logistic regression                               Number of obs   =     423726
                                                  Average RVI     =     0.0373
                                                  Largest FMI     =     0.2090
DF adjustment:   Large sample                     DF:     min     =     450.12
                                                          avg     = 6765652.48
                                                          max     =   2.78e+07
Model F test:       Equal FMI                     F(  11,229620.5)=     185.20
Within VCE type:       Robust                     Prob > F        =     0.0000

                            (Within VCE adjusted for 13538 clusters in dyadid)
------------------------------------------------------------------------------
     midongl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  lnlifepenl |  -.2393357   .0454194    -5.27   0.000    -.3285961   -.1500753
       h10dm |  -2.138834   .3298128    -6.48   0.000    -2.785262   -1.492406
         dmh |   .0308464   .0088544     3.48   0.000      .013492    .0482008
      lncprt |  -.2485966   .0474471    -5.24   0.000    -.3415912   -.1556021
        mjpw |     2.1098   .2023427    10.43   0.000     1.713216    2.506385
     contigl |  -3.407116   .6951962    -4.90   0.000    -4.769676   -2.044557
      lndist |  -.8172734    .090955    -8.99   0.000    -.9955419   -.6390049
    numstate |   .0054734   .0011472     4.77   0.000     .0032248     .007722
    midyears |  -.5728288   .0279414   -20.50   0.000     -.627593   -.5180647
   midyears2 |   .0244947   .0015708    15.59   0.000     .0214159    .0275734
   midyears3 |  -.0003006   .0000245   -12.28   0.000    -.0003486   -.0002527
       _cons |   .5660986   .8210845     0.69   0.491    -1.043306    2.175503
------------------------------------------------------------------------------
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Imputations (20):
  .........10.........20 done

Multiple-imputation estimates                     Imputations     =         20
Logistic regression                               Number of obs   =     409703
                                                  Average RVI     =     0.0207
                                                  Largest FMI     =     0.1160
DF adjustment:   Large sample                     DF:     min     =    1443.21
                                                          avg     =   8.21e+08
                                                          max     =   6.38e+09
Model F test:       Equal FMI                     F(  10,439864.8)=      99.47
Within VCE type:       Robust                     Prob > F        =     0.0000

                            (Within VCE adjusted for 13273 clusters in dyadid)
------------------------------------------------------------------------------
    fmidonsl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  lnlifepenl |  -.2965657    .092192    -3.22   0.001    -.4774103    -.115721
       h10dm |          0  (omitted)
         dmh |   .0300944   .0239727     1.26   0.209    -.0168912      .07708
      lncprt |  -.3610502    .101148    -3.57   0.000    -.5592966   -.1628038
        mjpw |   1.652527   .4466715     3.70   0.000     .7770662    2.527987
     contigl |   3.270793   2.657218     1.23   0.218    -1.937259    8.478845
      lndist |  -.1759591    .337681    -0.52   0.602    -.8378017    .4858835
    numstate |   .0073951   .0029314     2.52   0.012     .0016494    .0131407
   fmidyears |   -.451082   .0752562    -5.99   0.000    -.5985815   -.3035826
  fmidyears2 |   .0156269   .0043308     3.61   0.000     .0071387     .024115
  fmidyears3 |   -.000156   .0000662    -2.36   0.018    -.0002857   -.0000263
       _cons |  -8.509265   2.794399    -3.05   0.002    -13.98623   -3.032305
------------------------------------------------------------------------------
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Imputations (20):
  .........10.........20 done

Multiple-imputation estimates                     Imputations     =         20
Logistic regression                               Number of obs   =     410397
                                                  Average RVI     =     0.0936
                                                  Largest FMI     =     0.3839
DF adjustment:   Large sample                     DF:     min     =     135.20
                                                          avg     =   3.61e+07
                                                          max     =   1.91e+08
Model F test:       Equal FMI                     F(  10,23540.8) =      87.36
Within VCE type:       Robust                     Prob > F        =     0.0000

                            (Within VCE adjusted for 13273 clusters in dyadid)
------------------------------------------------------------------------------
    fmidongl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  lnlifepenl |  -.2902391   .0584861    -4.96   0.000    -.4059051    -.174573
       h10dm |          0  (omitted)
         dmh |    .026967   .0143474     1.88   0.060    -.0011535    .0550876
      lncprt |  -.3469767   .0671633    -5.17   0.000    -.4786142   -.2153391
        mjpw |   2.148508    .319476     6.73   0.000     1.522346     2.77467
     contigl |  -.8489106   1.337772    -0.63   0.526    -3.470895    1.773073
      lndist |  -.5233707   .1688146    -3.10   0.002    -.8542413   -.1925001
    numstate |   .0020414   .0017094     1.19   0.232    -.0013095    .0053922
   fmidyears |  -.6481117   .0444369   -14.58   0.000    -.7352064   -.5610171
  fmidyears2 |   .0266736    .002288    11.66   0.000     .0221891    .0311581
  fmidyears3 |  -.0003048   .0000329    -9.27   0.000    -.0003693   -.0002403
       _cons |  -2.344262   1.513065    -1.55   0.121     -5.31007     .621546
------------------------------------------------------------------------------
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Imputations (20):
  .........10.........20 done

Multiple-imputation estimates                     Imputations     =         20
Logistic regression                               Number of obs   =     423001
                                                  Average RVI     =     0.0259
                                                  Largest FMI     =     0.1667
DF adjustment:   Large sample                     DF:     min     =     703.46
                                                          avg     =   3.63e+07
                                                          max     =   4.04e+08
Model F test:       Equal FMI                     F(  12,509333.4)=     163.28
Within VCE type:       Robust                     Prob > F        =     0.0000

                            (Within VCE adjusted for 13538 clusters in dyadid)
------------------------------------------------------------------------------
     midonsl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       CIElc |  -.1765308    .058691    -3.01   0.003    -.2917613   -.0613003
       h10dm |  -2.033429   .6229951    -3.26   0.001    -3.254502   -.8123566
  h10dmCIElc |   .1369379   .2236794     0.61   0.540    -.3014781     .575354
         dmh |    .041007   .0097278     4.22   0.000     .0219406    .0600733
      lncprt |  -.2255375   .0567074    -3.98   0.000    -.3366819   -.1143931
        mjpw |   1.920228   .2259435     8.50   0.000     1.477386    2.363069
     contigl |  -4.439272    .723675    -6.13   0.000     -5.85765   -3.020893
      lndist |  -.9944495   .0978376   -10.16   0.000    -1.186208   -.8026912
    numstate |   .0053506   .0013491     3.97   0.000     .0027058    .0079955
    midyears |  -.3985603   .0276514   -14.41   0.000    -.4527561   -.3443645
   midyears2 |   .0157667   .0015372    10.26   0.000     .0127539    .0187795
   midyears3 |  -.0001838   .0000235    -7.81   0.000    -.0002299   -.0001376
       _cons |   2.394508   .7400697     3.24   0.001     .9439977    3.845019
------------------------------------------------------------------------------
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Imputations (20):
  .........10.........20 done

Multiple-imputation estimates                     Imputations     =         20
Logistic regression                               Number of obs   =     423726
                                                  Average RVI     =     0.0401
                                                  Largest FMI     =     0.2481
DF adjustment:   Large sample                     DF:     min     =     320.74
                                                          avg     =   2.97e+07
                                                          max     =   3.23e+08
Model F test:       Equal FMI                     F(  12,200718.9)=     166.04
Within VCE type:       Robust                     Prob > F        =     0.0000

                            (Within VCE adjusted for 13538 clusters in dyadid)
------------------------------------------------------------------------------
     midongl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       CIElc |  -.1862421   .0571265    -3.26   0.001    -.2986322   -.0738521
       h10dm |  -2.430913   .6172653    -3.94   0.000    -3.640756    -1.22107
  h10dmCIElc |   .1786802   .2153599     0.83   0.407    -.2434463    .6008067
         dmh |    .032636   .0092243     3.54   0.000     .0145564    .0507156
      lncprt |  -.2572854    .048352    -5.32   0.000    -.3520535   -.1625172
        mjpw |   2.122464   .2079872    10.20   0.000     1.714817    2.530112
     contigl |  -3.609055   .6973253    -5.18   0.000     -4.97579    -2.24232
      lndist |   -.846879   .0912384    -9.28   0.000    -1.025703   -.6680547
    numstate |   .0058156   .0012084     4.81   0.000     .0034457    .0081855
    midyears |  -.5729556   .0278367   -20.58   0.000    -.6275145   -.5183966
   midyears2 |   .0245781   .0015721    15.63   0.000     .0214969    .0276593
   midyears3 |  -.0003025   .0000246   -12.30   0.000    -.0003508   -.0002543
       _cons |   2.120365   .7046833     3.01   0.003       .73921     3.50152
------------------------------------------------------------------------------
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Imputations (20):
  .........10.........20 done

Multiple-imputation estimates                     Imputations     =         20
Logistic regression                               Number of obs   =     409703
                                                  Average RVI     =     0.0439
                                                  Largest FMI     =     0.1840
DF adjustment:   Large sample                     DF:     min     =     578.77
                                                          avg     =   5.04e+07
                                                          max     =   3.01e+08
Model F test:       Equal FMI                     F(  10,138955.7)=      64.66
Within VCE type:       Robust                     Prob > F        =     0.0000

                            (Within VCE adjusted for 13273 clusters in dyadid)
------------------------------------------------------------------------------
    fmidonsl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       CIElc |  -.5048975   .1387211    -3.64   0.000    -.7773556   -.2324395
       h10dm |          0  (omitted)
  h10dmCIElc |          0  (omitted)
         dmh |   .0421948   .0264683     1.59   0.111    -.0096822    .0940719
      lncprt |  -.3582804   .1089066    -3.29   0.001    -.5717336   -.1448273
        mjpw |   1.637223   .4688635     3.49   0.000      .718266     2.55618
     contigl |   2.814243   2.763883     1.02   0.309    -2.602867    8.231354
      lndist |  -.2374112   .3490486    -0.68   0.496    -.9215338    .4467114
    numstate |   .0114327   .0036877     3.10   0.002     .0042025    .0186629
   fmidyears |  -.4454535   .0751847    -5.92   0.000    -.5928127   -.2980943
  fmidyears2 |   .0152993   .0043395     3.53   0.000      .006794    .0238046
  fmidyears3 |  -.0001509   .0000665    -2.27   0.023    -.0002811   -.0000206
       _cons |  -7.271326   2.763379    -2.63   0.009    -12.68746   -1.855193
------------------------------------------------------------------------------
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Imputations (20):
  .........10.........20 done

Multiple-imputation estimates                     Imputations     =         20
Logistic regression                               Number of obs   =     410397
                                                  Average RVI     =     0.1043
                                                  Largest FMI     =     0.3658
DF adjustment:   Large sample                     DF:     min     =     148.78
                                                          avg     = 3950686.88
                                                          max     =   1.31e+07
Model F test:       Equal FMI                     F(  10,25733.1) =      66.02
Within VCE type:       Robust                     Prob > F        =     0.0000

                            (Within VCE adjusted for 13273 clusters in dyadid)
------------------------------------------------------------------------------
    fmidongl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       CIElc |  -.4202107   .0881976    -4.76   0.000    -.5944924   -.2459289
       h10dm |          0  (omitted)
  h10dmCIElc |          0  (omitted)
         dmh |   .0359268   .0153056     2.35   0.019     .0059279    .0659256
      lncprt |  -.3576419   .0709747    -5.04   0.000    -.4967498    -.218534
        mjpw |   2.191131   .3281274     6.68   0.000     1.548013    2.834249
     contigl |  -1.351388   1.338011    -1.01   0.312    -3.973843    1.271066
      lndist |   -.592172   .1673924    -3.54   0.000    -.9202552   -.2640887
    numstate |     .00475   .0020602     2.31   0.021     .0007077    .0087923
   fmidyears |  -.6453015   .0442592   -14.58   0.000    -.7320479    -.558555
  fmidyears2 |   .0265386   .0022844    11.62   0.000     .0220612     .031016
  fmidyears3 |  -.0003027   .0000329    -9.19   0.000    -.0003672   -.0002381
       _cons |   -.773026    1.36239    -0.57   0.570    -3.443271    1.897219
------------------------------------------------------------------------------
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. 
. 
. order specification specnumber n h10dmCoefficient h10dmSE h10dmpvalues CIElCoefficient CIElSE CIElpvalues

. replace h10dmpvalues=2*normal(-abs(h10dmCoefficient/h10dmSE))
(16 real changes made)

. replace CIElpvalues=2*normal(-abs(CIElCoefficient/CIElSE))
(32 real changes made)

. keep specification specnumber n h10dmCoefficient h10dmSE h10dmpvalues CIElCoefficient CIElSE CIElpvalues

. drop if specnumber==.
(436509 observations deleted)

. 
. saveold "robustnessMh10dm.dta", replace
(note: file robustnessMh10dm.dta not found)
file robustnessMh10dm.dta saved

. 
. 
. 
. 
. 
end of do-file

. *Produces: robustnessMh10dm.dta
. 
. ***Outputting final data***
. clear

. use "robustness1.dta"

. append using "robustnessMdml.dta"

. append using "robustness3.dta"

. replace specnumber=_n
(40 real changes made)

. format DmlCoefficient %12.6f

. format DmlSE %12.6f

. format CIElCoefficient %12.6f

. format CIElSE %12.6f

. format Dmlpvalues %12.6f

. format CIElpvalues %12.6f

. saveold "pvalues.dta", replace
(note: file pvalues.dta not found)
file pvalues.dta saved

. 
. 
. **Final Data for bdm
. 
. clear

. use "robustnessbdm.dta"

. append using "robustnessbdm2.dta"

. append using "robustnessMbdm.dta"
specification was str5 now str6

. * replace specification=specification+"'" if _n>23
. replace specnumber=_n
(40 real changes made)

. format bdmCoefficient %12.6f

. format bdmSE %12.6f

. format CIElCoefficient %12.6f

. format CIElSE %12.6f

. format bdmpvalues %12.6f

. format CIElpvalues %12.6f

. saveold "pvaluesbdm.dta", replace
(note: file pvaluesbdm.dta not found)
file pvaluesbdm.dta saved

. 
. 
. **Final Data for h10dm
. clear

. use "robustnessh10dm.dta"

. append using "robustnessh10dm2.dta"

. append using "robustnessMh10dm.dta"

. * replace specification=specification+"'" if _n<=23
. * replace specification=specification+"''" if _n>23
. replace specnumber=_n
(40 real changes made)

. format h10dmCoefficient %12.6f

. format h10dmSE %12.6f

. format CIElCoefficient %12.6f

. format CIElSE %12.6f

. format h10dmpvalues %12.6f

. format CIElpvalues %12.6f

. saveold "pvaluesh10dm.dta", replace
(note: file pvaluesh10dm.dta not found)
file pvaluesh10dm.dta saved

. 
. capture log close
